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1. A non-transitory computer storage medium storing computer-useable instructions that, when used by one or more computing devices, cause the one or more computing devices to perform operations comprising: sampling data, via a social engine, from one or more social media streams, in accordance with a user selection received via a user device; assigning part-of-speech (POS) tags to text in the data; applying natural language processing, by a trending topic tool, to extract candidate topics from the data using a first rule comprising: identifying a sequence of a plurality of the assigned POS tags, wherein each POS tag of the sequence is selected from a group consisting of at least one of a proper noun tag, a plural proper noun tag, or a cardinal number tag; defining topic boundaries based on the identified sequence; and extracting a portion of the text corresponding to the topic boundaries as one of the candidate topics; ranking the candidate topics, by the trending topic tool, with a relevance score that quantifies relative importance of each candidate topic to determine trending topics; classifying, by the trending topic tool, the trending topics into categories; grouping the candidate topics into topic clusters of semantically-similar topics, by the trending topic tool, and transmitting the classified and clustered trending topics for display on the user device.

1. A non-transitory computer storage medium storing computer-useable instructions that, when used by one or more computing devices, cause the one or more computing devices to perform operations comprising: sampling data, via a social engine, from one or more social media streams, in accordance with a user selection received via a user device; assigning part-of-speech (POS) tags to text in the data; applying natural language processing, by a trending topic tool, to extract candidate topics from the data using a first rule comprising: identifying a sequence of a plurality of the assigned POS tags, wherein each POS tag of the sequence is selected from a group consisting of at least one of a proper noun tag, a plural proper noun tag, or a cardinal number tag; defining topic boundaries based on the identified sequence; and extracting a portion of the text corresponding to the topic boundaries as one of the candidate topics; ranking the candidate topics, by the trending topic tool, with a relevance score that quantifies relative importance of each candidate topic to determine trending topics; classifying, by the trending topic tool, the trending topics into categories; grouping the candidate topics into topic clusters of semantically-similar topics, by the trending topic tool, and transmitting the classified and clustered trending topics for display on the user device. 9. The non-transitory computer storage medium of claim 1 , wherein ranking the candidate topics with a relevance score comprises: determining an Accumulated Term Frequency (ATF) for a candidate topic in a document of the data, the ATF counting an occurrence of the candidate topic once for each document in which the candidate topic appears; determining an Inverse Document Frequency (IDF) for the candidate topic in the data; and determining the relevance score for the candidate topic based on the ATF and the IDF for the candidate topic.

1. A method, comprising: receiving, by one or more computers, a current search query during a current search session; determining, by one or more computers and based on a difference between terms of the current search query and terms of a previous search query received during the current search session, that the current search query is an attempt to refine the previous search query; identifying, by one or more computers and based on the determination that the current search query is an attempt to refine the previous search query, a set of related terms based on a given term that is included in the previous search query, but not included in the current search query; determining, by one or more computers and based on search log data for previous search sessions, that the given term was replaced with a different term from the set of related terms in at least a specified portion of the previous search sessions; in response to the determination that the given term was replaced with the different term from the set of related terms, generating a modified search query that includes the different term; and providing the modified search query for presentation in a display of a user device corresponding to the current search session.

1. A method, comprising: receiving, by one or more computers, a current search query during a current search session; determining, by one or more computers and based on a difference between terms of the current search query and terms of a previous search query received during the current search session, that the current search query is an attempt to refine the previous search query; identifying, by one or more computers and based on the determination that the current search query is an attempt to refine the previous search query, a set of related terms based on a given term that is included in the previous search query, but not included in the current search query; determining, by one or more computers and based on search log data for previous search sessions, that the given term was replaced with a different term from the set of related terms in at least a specified portion of the previous search sessions; in response to the determination that the given term was replaced with the different term from the set of related terms, generating a modified search query that includes the different term; and providing the modified search query for presentation in a display of a user device corresponding to the current search session. 8. The method of claim 1 , further comprising obtaining search results based on the modified search query.

15. A method of a server, comprising: receiving one or more encrypted first documents from a client device, each encrypted first document being an encryption of a corresponding first document, wherein each of the one or more encrypted first documents comprises dictionary data comprising a word-level encryption of each first document, the word-level encryption comprising encrypted words, the dictionary data also comprising a sentence-level encryption of each first document, the sentence-level encryption comprising encrypted sentences, wherein at least one encrypted sentence is a sentence-level encryption of a first sentence comprising a plurality of first words, and the encrypted words comprise a word-level encryption of each first word; wherein each first document comprises one or more first sentences each of which comprises one or more first words, and at least one first document comprises a plurality of first sentences at least one of which comprises a plurality of first words; wherein the sentence-level encryption comprises a plurality of encrypted sentences each of which is an encryption of a corresponding one of the first sentences, each first sentence corresponding to an encrypted sentence; and the word-level encryption comprises a plurality of encrypted words each of which is an encryption of a corresponding one of the first words, each first word corresponding to an encrypted word; determining index data in one or more storage networks, wherein the index data comprises encrypted word frequencies and encrypted word position identifiers based on the encrypted words of the encrypted documents; receiving one or more encrypted search terms from the client device; identifying one or more of the encrypted documents in the one or more storage networks based on a search performed with the one or more encrypted search terms and at least one of the encrypted word frequencies and the encrypted word position identifiers.

15. A method of a server, comprising: receiving one or more encrypted first documents from a client device, each encrypted first document being an encryption of a corresponding first document, wherein each of the one or more encrypted first documents comprises dictionary data comprising a word-level encryption of each first document, the word-level encryption comprising encrypted words, the dictionary data also comprising a sentence-level encryption of each first document, the sentence-level encryption comprising encrypted sentences, wherein at least one encrypted sentence is a sentence-level encryption of a first sentence comprising a plurality of first words, and the encrypted words comprise a word-level encryption of each first word; wherein each first document comprises one or more first sentences each of which comprises one or more first words, and at least one first document comprises a plurality of first sentences at least one of which comprises a plurality of first words; wherein the sentence-level encryption comprises a plurality of encrypted sentences each of which is an encryption of a corresponding one of the first sentences, each first sentence corresponding to an encrypted sentence; and the word-level encryption comprises a plurality of encrypted words each of which is an encryption of a corresponding one of the first words, each first word corresponding to an encrypted word; determining index data in one or more storage networks, wherein the index data comprises encrypted word frequencies and encrypted word position identifiers based on the encrypted words of the encrypted documents; receiving one or more encrypted search terms from the client device; identifying one or more of the encrypted documents in the one or more storage networks based on a search performed with the one or more encrypted search terms and at least one of the encrypted word frequencies and the encrypted word position identifiers. 18. The method of claim 15 , further comprising determining one or more permutations of the encrypted search terms, and wherein identifying the one or more encrypted documents is based further on the search performed with the one or more permutations of the encrypted search terms.

3. The method according claim 1 , wherein the desired level of personalization is selected from a range of personalization levels between a personalized level and a global level that is not personalized.

3. The method according claim 1 , wherein the desired level of personalization is selected from a range of personalization levels between a personalized level and a global level that is not personalized. 4. The method according to claim 3 , wherein the range of personalization levels includes a community level between the personalized level and the global level.

42. A method comprising: with a mobile station: transmitting one or more signals representing a request for translation information from a translation information service, wherein said translation information is associated with a location and one or more written and/or spoken languages; receiving one or more signals representing a response comprising requested translation information, said requested translation information being based, at least in part, on said request for translation information, said location, and predicted information, wherein the predicted information is associated with the request for translation information, the location, and at least one other request for translation information associated with at least one other location and previously transmitted to said translation information service by at least one other mobile station; and generating a presentation for a user based, at least in part, on said response.

42. A method comprising: with a mobile station: transmitting one or more signals representing a request for translation information from a translation information service, wherein said translation information is associated with a location and one or more written and/or spoken languages; receiving one or more signals representing a response comprising requested translation information, said requested translation information being based, at least in part, on said request for translation information, said location, and predicted information, wherein the predicted information is associated with the request for translation information, the location, and at least one other request for translation information associated with at least one other location and previously transmitted to said translation information service by at least one other mobile station; and generating a presentation for a user based, at least in part, on said response. 49. The method as recited in claim 42 , wherein said predicted information comprises statistical information based, at least in part, on a plurality of other requests for translation information associated with said location, the at least one other location, and previously transmitted to said translation information service by a plurality of other mobile stations.

13. A communication system comprising: an audio visual asset recorded by a first user using a predetermined script to communicate with a second user; at least one audio visual segment created by partitioning the audio visual asset such that a variable final message compilation is anticipated; at least one of a naming paradigm and a data tagging system to tag the at least one audio visual segment such that the at least one audio visual segment is accessible via an audio visual data tag and is exported to the variable final message compilation; a multimedia synthesis compiler to compile the variable final message compilation upon uploading of the at least one audio visual segment into the multimedia synthesis compiler; and a message sent to the second user from the variable final message compilation based on the at least one audio visual segment.

13. A communication system comprising: an audio visual asset recorded by a first user using a predetermined script to communicate with a second user; at least one audio visual segment created by partitioning the audio visual asset such that a variable final message compilation is anticipated; at least one of a naming paradigm and a data tagging system to tag the at least one audio visual segment such that the at least one audio visual segment is accessible via an audio visual data tag and is exported to the variable final message compilation; a multimedia synthesis compiler to compile the variable final message compilation upon uploading of the at least one audio visual segment into the multimedia synthesis compiler; and a message sent to the second user from the variable final message compilation based on the at least one audio visual segment. 14. The system of claim 13 wherein the recording of the audio visual asset using the predetermined script is performed using at least one of a microphone, an interactive voice response, and the first user.

27. A computer-implemented method that facilitates data handling, comprising: accepting data, wherein the data is a message sent to a user from another user; analyzing the data to determine an associated intent, wherein the intent includes a confidence value of the accuracy of the determined intent; reformulating at least part of the data to produce a description of the intent; and selectively presenting the description to the user based upon the confidence value associated with the intent exceeding a dynamically generated threshold, the value indicating whether to respond to the data, the threshold is dynamically generated based upon at least an inferred current state of the user based upon observation of the user.

27. A computer-implemented method that facilitates data handling, comprising: accepting data, wherein the data is a message sent to a user from another user; analyzing the data to determine an associated intent, wherein the intent includes a confidence value of the accuracy of the determined intent; reformulating at least part of the data to produce a description of the intent; and selectively presenting the description to the user based upon the confidence value associated with the intent exceeding a dynamically generated threshold, the value indicating whether to respond to the data, the threshold is dynamically generated based upon at least an inferred current state of the user based upon observation of the user. 28. The method of claim 27 , further comprising providing the analysis to a machine-learned classifier to generate a probability indicating whether to respond to the data.

11. A computer-implemented method for improved zero-day malware detection comprising: receiving, at a computer that includes one or more processors and memory, a set of training files which are each known to be either malign or benign, wherein the training files comprise one or more types of computer files; analyzing, using the one or more computer processors, the set of training files to determine features of the training files, wherein the analyzing determines n-gram features; receiving, using the one or more computer processors, a feature set description that includes a semantic label for each attribute class present in the training files and a set of corresponding attributes that make up the attribute class; generating, using the one or more computer processors, a plurality of attribute class-specific feature vectors (FVs) for the training files using the determined n-gram features and the feature set description, wherein the FVs are vectors of n-gram features present in malign files of the attribute class; concatenating, using the one or more computer processors, the plurality of attribute class-specific FVs into an extended feature vector (EFV) for the training files; and generating, using the one or more computer processors, a target file classifier based on the EFV using a plurality of classifier algorithms.

11. A computer-implemented method for improved zero-day malware detection comprising: receiving, at a computer that includes one or more processors and memory, a set of training files which are each known to be either malign or benign, wherein the training files comprise one or more types of computer files; analyzing, using the one or more computer processors, the set of training files to determine features of the training files, wherein the analyzing determines n-gram features; receiving, using the one or more computer processors, a feature set description that includes a semantic label for each attribute class present in the training files and a set of corresponding attributes that make up the attribute class; generating, using the one or more computer processors, a plurality of attribute class-specific feature vectors (FVs) for the training files using the determined n-gram features and the feature set description, wherein the FVs are vectors of n-gram features present in malign files of the attribute class; concatenating, using the one or more computer processors, the plurality of attribute class-specific FVs into an extended feature vector (EFV) for the training files; and generating, using the one or more computer processors, a target file classifier based on the EFV using a plurality of classifier algorithms. 13. The method of claim 11 further comprising: receiving, using the one or more computer processors, a target, unknown computer file; analyzing, using the one or more computer processors, the target, unknown computer file to determine features of the target, unknown file; generating, using the one or more computer processors, a plurality of attribute class-specific FVs of the target, unknown computer file using the determined features of the target, unknown file; concatenating, using the one or more computer processors, the plurality attribute class-specific FVs of the target, unknown computer file into an EFV for the target, unknown computer file; and classifying, using the one or more computer processors, the target, unknown computer file as malign or benign by applying the target file classifier to the EFV of the target, unknown computer file.

20. A speech recognition process comprising the rejection grammar process as set forth in claim 18 further comprising the steps of: comparing the digitized sequential representation of the utterance to entries in a main grammar using an accepted main grammar process; calculating a second set of probabilities from said comparing step; determining a highest probability from each of the first and second sets of probabilities; comparing the highest probabilities from each of the first and second sets of probabilities; if the highest probability from the first and second sets of probabilities is found in the first set of probabilities, rejecting the utterance; and if the highest probability from the first and second sets of probabilities is found in the second set of probabilities, accepting the utterance.

20. A speech recognition process comprising the rejection grammar process as set forth in claim 18 further comprising the steps of: comparing the digitized sequential representation of the utterance to entries in a main grammar using an accepted main grammar process; calculating a second set of probabilities from said comparing step; determining a highest probability from each of the first and second sets of probabilities; comparing the highest probabilities from each of the first and second sets of probabilities; if the highest probability from the first and second sets of probabilities is found in the first set of probabilities, rejecting the utterance; and if the highest probability from the first and second sets of probabilities is found in the second set of probabilities, accepting the utterance. 21. The process of claim 20 further comprising the step of Assigning an unequal weight said first set of probabilities and said second set of probabilities unequally.

26. A computer-readable storage medium having executable instructions for causing a client to manage web history data and use context menus pulled up in one of a tree text history section and a web document section and within the context of a search term during at least one of a search and a sub-search by a user and use a dialog box to select a display option for any sub-search results, the client comprising computer-readable program code for causing the client to: (a) connect with a server in a web environment; (b) request search results from the server; (c) receive search results from the server; (d) automatically and repeatedly create tree text history entries in the tree text history section in a hierarchical format by associating with the web history data received from the at least one of the search and the sub-search within the context of the search term; and (e) store the tree text history entries in a memory; wherein the tree text history management system provides the user with tree text history entries created within the context of the search terms that are reusable, modifiable, and manageable, and wherein the context menu is integrated with the tree text history management system.

26. A computer-readable storage medium having executable instructions for causing a client to manage web history data and use context menus pulled up in one of a tree text history section and a web document section and within the context of a search term during at least one of a search and a sub-search by a user and use a dialog box to select a display option for any sub-search results, the client comprising computer-readable program code for causing the client to: (a) connect with a server in a web environment; (b) request search results from the server; (c) receive search results from the server; (d) automatically and repeatedly create tree text history entries in the tree text history section in a hierarchical format by associating with the web history data received from the at least one of the search and the sub-search within the context of the search term; and (e) store the tree text history entries in a memory; wherein the tree text history management system provides the user with tree text history entries created within the context of the search terms that are reusable, modifiable, and manageable, and wherein the context menu is integrated with the tree text history management system. 29. The computer-readable storage medium of claim 26 , wherein the computer-readable program code is a browser plug-in.

1. A computer program product, tangibly embodied in a computer-readable storage medium, the computer program product being operable to cause data processing apparatus to perform operations comprising: receiving data characterizing a query containing a first plurality of elements and characterizing context information related to the query, at least a portion of the first plurality of elements being in a first language; associating the portion of the first plurality of elements with a second plurality of elements, at least a portion of the second plurality of elements being in a second language, wherein a subset of the first plurality of elements in the first language do not have associated elements in the second language; performing a search using the second plurality of elements in the second language and the subset of the first plurality of elements in the first language to identify one or more results; and initiating a presentation of the results; wherein the first and second plurality of elements comprise alphanumeric search terms other than search term operators; wherein the receiving comprises receiving context information related to the query, the context information being used to filter elements of the second language to be associated with the first plurality of elements.

1. A computer program product, tangibly embodied in a computer-readable storage medium, the computer program product being operable to cause data processing apparatus to perform operations comprising: receiving data characterizing a query containing a first plurality of elements and characterizing context information related to the query, at least a portion of the first plurality of elements being in a first language; associating the portion of the first plurality of elements with a second plurality of elements, at least a portion of the second plurality of elements being in a second language, wherein a subset of the first plurality of elements in the first language do not have associated elements in the second language; performing a search using the second plurality of elements in the second language and the subset of the first plurality of elements in the first language to identify one or more results; and initiating a presentation of the results; wherein the first and second plurality of elements comprise alphanumeric search terms other than search term operators; wherein the receiving comprises receiving context information related to the query, the context information being used to filter elements of the second language to be associated with the first plurality of elements. 2. The product of claim 1 , wherein the operations further comprise presenting to a user suggested elements in the second language, wherein the associating is performed in response to a user selection of the second plurality of elements.

8. A method, comprising: receiving a negotiated meta-model over a computer network from a meta-model negotiation service, the negotiated meta-model describing collaborations between trading partners and incorporating more than two negotiated meta-model elements selected from a stored first and second set of meta-model elements, the first set of one or more meta-model elements comprising one or more supply chain elements, the one or more supply chain elements comprising one or more site supply chain elements representing one or more sites of a supply chain network that produces one or more items, one or more resource supply chain elements representing one or more resources of the supply chain network, and one or more buffer supply chain elements representing one or more buffers of the supply chain network, the second set of one or more meta-model elements representing the semantics of a machine-actionable collaboration standard, the semantics comprising a nature of a demand signal representing a demand and a software protocol used to communicate the demand signal, the more than two negotiated meta-model elements describing a private collaboration standard unique to the trading partners for collaboration between the trading partners, the negotiated meta-model having been negotiated by the associated trading partner and the one or more other trading partners using the meta-model negotiation service; determining the semantics of the negotiated meta-model subsequent to negotiation of the negotiated meta-model, the semantics capable of being understood by collaboration software associated with each of the trading partners independent of any modification of the collaboration software; automatically collaborating with the one or more other trading partners based on the standard for collaborations reflected in the negotiated meta-model; and in response to receiving over the computer network the demand signal for the one or more items based on the negotiated meta-model and in accordance with the semantics of the negotiated meta-model, shipping, by at least one of the one or more trading partners, the one or more items in accordance with the demand and the negotiated meta-model.

8. A method, comprising: receiving a negotiated meta-model over a computer network from a meta-model negotiation service, the negotiated meta-model describing collaborations between trading partners and incorporating more than two negotiated meta-model elements selected from a stored first and second set of meta-model elements, the first set of one or more meta-model elements comprising one or more supply chain elements, the one or more supply chain elements comprising one or more site supply chain elements representing one or more sites of a supply chain network that produces one or more items, one or more resource supply chain elements representing one or more resources of the supply chain network, and one or more buffer supply chain elements representing one or more buffers of the supply chain network, the second set of one or more meta-model elements representing the semantics of a machine-actionable collaboration standard, the semantics comprising a nature of a demand signal representing a demand and a software protocol used to communicate the demand signal, the more than two negotiated meta-model elements describing a private collaboration standard unique to the trading partners for collaboration between the trading partners, the negotiated meta-model having been negotiated by the associated trading partner and the one or more other trading partners using the meta-model negotiation service; determining the semantics of the negotiated meta-model subsequent to negotiation of the negotiated meta-model, the semantics capable of being understood by collaboration software associated with each of the trading partners independent of any modification of the collaboration software; automatically collaborating with the one or more other trading partners based on the standard for collaborations reflected in the negotiated meta-model; and in response to receiving over the computer network the demand signal for the one or more items based on the negotiated meta-model and in accordance with the semantics of the negotiated meta-model, shipping, by at least one of the one or more trading partners, the one or more items in accordance with the demand and the negotiated meta-model. 9. The method of claim 8 , wherein the negotiated meta-model is machine-actionable and reflects a private, differentiated standard for collaboration customized for the one or more trading partners.

1. A computer system for processing a text search query in a collection of documents, comprising: a processor; a memory embedded with computer instructions executed by said processor, said computer instructions performing operations comprising: generating a full posting index for said documents of said collection, wherein said generated full posting index comprises a first set of index terms and a full posting list for each index term of said first set, and enumerates occurrences of each index term in said documents of said collection; receiving a text search query; translating one or more search conditions on one or more search terms of said received text search query into one or more conditions on said index terms of said first set, wherein said one or more translated conditions comprises one or more search terms; generating a short posting index for said documents of said collection, said short posting index comprising a second set of index terms and a short posting list for said index terms of said second set, enumerating documents in which said index term of said second set occurs; generating one or more filter conditions, comprising one or more Boolean conditions, and one or more complementary conditions to represent a full content of said one or more translated conditions, wherein said one or more generated filter conditions approximate said one or more translated conditions, wherein one or more filter conditions is generated according to availability of index terms of said second set in said generated short posting index; and generating a query result corresponding to said full content of said one or more translated conditions, wherein said generated query result is based on said generated short posting index, said one or more generated filter conditions, said full posting index and said one or more generated complementary conditions.

1. A computer system for processing a text search query in a collection of documents, comprising: a processor; a memory embedded with computer instructions executed by said processor, said computer instructions performing operations comprising: generating a full posting index for said documents of said collection, wherein said generated full posting index comprises a first set of index terms and a full posting list for each index term of said first set, and enumerates occurrences of each index term in said documents of said collection; receiving a text search query; translating one or more search conditions on one or more search terms of said received text search query into one or more conditions on said index terms of said first set, wherein said one or more translated conditions comprises one or more search terms; generating a short posting index for said documents of said collection, said short posting index comprising a second set of index terms and a short posting list for said index terms of said second set, enumerating documents in which said index term of said second set occurs; generating one or more filter conditions, comprising one or more Boolean conditions, and one or more complementary conditions to represent a full content of said one or more translated conditions, wherein said one or more generated filter conditions approximate said one or more translated conditions, wherein one or more filter conditions is generated according to availability of index terms of said second set in said generated short posting index; and generating a query result corresponding to said full content of said one or more translated conditions, wherein said generated query result is based on said generated short posting index, said one or more generated filter conditions, said full posting index and said one or more generated complementary conditions. 3. The system according to claim 1 further comprising: a weighted index term frequency in each document of said collection in said short posting index.

17. A system comprising: a data store storing label data that specifies a set of initial labels for a non-text content item, wherein the non-text content item is associated with each of a plurality of web pages, and wherein each initial label includes one or more words; and one or more computers coupled to the data store, the one or more computers storing instructions that cause the one or more computers to interact with the data store and perform operations comprising: identifying a non-text content item that is associated with each of a plurality of web pages; receiving label data that includes a set of initial labels for the non-text content item, wherein each initial label includes one or more words; grouping, for each of two or more sets of matching web pages among the plurality of web pages, initial labels that are associated with the set of matching web pages into a label group, the initial labels for different set of matching web pages being grouped to different label groups; grouping different sets of matching labels from the set of initial labels into different label groups; and selecting, as a final label for the non-text content item, an n-gram of one or more words that is included in at least a threshold number of different label groups.

17. A system comprising: a data store storing label data that specifies a set of initial labels for a non-text content item, wherein the non-text content item is associated with each of a plurality of web pages, and wherein each initial label includes one or more words; and one or more computers coupled to the data store, the one or more computers storing instructions that cause the one or more computers to interact with the data store and perform operations comprising: identifying a non-text content item that is associated with each of a plurality of web pages; receiving label data that includes a set of initial labels for the non-text content item, wherein each initial label includes one or more words; grouping, for each of two or more sets of matching web pages among the plurality of web pages, initial labels that are associated with the set of matching web pages into a label group, the initial labels for different set of matching web pages being grouped to different label groups; grouping different sets of matching labels from the set of initial labels into different label groups; and selecting, as a final label for the non-text content item, an n-gram of one or more words that is included in at least a threshold number of different label groups. 20. The system of claim 17 , wherein the instructions cause the one or more computers to group different sets of matching labels from the set of initial labels into different label groups by performing operations comprising: identifying, from among the set of initial labels, a first set of at least two initial labels that have at least a threshold measure of similarity; grouping the first set of at least two initial labels into a first label group; identifying, from among the set of initial labels, a second set of at least two initial labels that have at least a threshold measure of similarity; and grouping the second set of at least two initial labels into a second label group.

7. The method of claim 4 , wherein: the generating of the summary records generates a map file that organizes the summary records into groups; and the presenting of at least the part of the full item record among the full item records is based on the map file that organizes the summary records into groups.

7. The method of claim 4 , wherein: the generating of the summary records generates a map file that organizes the summary records into groups; and the presenting of at least the part of the full item record among the full item records is based on the map file that organizes the summary records into groups. 10. The method of claim 7 , wherein: the map file organizes a portion of the summary records into a group that corresponds to a shipment availability indicated in the full item record; and the presenting of at least the part of the full item record is based on the group that corresponds to the shipment availability.

31. The method of claim 19 further comprising the step of displaying a plot in an area bounded by first, second and third non-parallel axes where each selected pair is represented by a point having a first coordinate along the first axis, a second coordinate along the second axis and a third coordinate along the third axis.

31. The method of claim 19 further comprising the step of displaying a plot in an area bounded by first, second and third non-parallel axes where each selected pair is represented by a point having a first coordinate along the first axis, a second coordinate along the second axis and a third coordinate along the third axis. 32. The method of claim 31 further comprising the steps of: wherein in the displaying step, the plot is a 3 dimensional visualization, the first coordinate is equal to the unique document index of the first document of a pair of documents and the second coordinate is equal to the unique document index of the second member of a pair of documents, and the third coordinate is equal to the first utility measure, and an icon representing the first utility measure is plotted for each pair of documents.

20. The method as recited in claim 17 , wherein a plurality of sub-segments are delineated within each said segment; and, a parametric mean of said adaptive decompositions over each said sub-segment is generated, each said sub-segment parametric mean being defined in terms of said representative set of decomposition atoms.

20. The method as recited in claim 17 , wherein a plurality of sub-segments are delineated within each said segment; and, a parametric mean of said adaptive decompositions over each said sub-segment is generated, each said sub-segment parametric mean being defined in terms of said representative set of decomposition atoms. 21. The method as recited in claim 20 , wherein said adaptive sparse approximation and parametric mean are carried out according to a greedy adaptive decomposition (GAD) process.

10. A method for remotely configuring a data access server in a system including a configuration editor and a configurable process control data access server and a server agent located remotely from the configuration editor on a computing node, the server agent being capable of identifying process control data access servers currently residing on the computing node, the method comprising the steps of: receiving, by the configuration editor from the server agent, a notification regarding data access servers presently residing on the computing node, including the configurable process control data access server; obtaining, by the configuration editor after receiving the notification, configuration information relating to the configurable process control data access server, the configuration information including: a configuration data set comprising data access server parameter values, and a rules data set comprising rules that guide processing the configuration data set; and providing access, by the configuration editor, to the configuration data set in accordance with the rules of the rules data set.

10. A method for remotely configuring a data access server in a system including a configuration editor and a configurable process control data access server and a server agent located remotely from the configuration editor on a computing node, the server agent being capable of identifying process control data access servers currently residing on the computing node, the method comprising the steps of: receiving, by the configuration editor from the server agent, a notification regarding data access servers presently residing on the computing node, including the configurable process control data access server; obtaining, by the configuration editor after receiving the notification, configuration information relating to the configurable process control data access server, the configuration information including: a configuration data set comprising data access server parameter values, and a rules data set comprising rules that guide processing the configuration data set; and providing access, by the configuration editor, to the configuration data set in accordance with the rules of the rules data set. 12. The method of claim 10 further comprising the step of: modifying, by the configuration editor, the configuration information relating to the configurable process control data access server.

1. An automatic correlation accelerator tool comprising: at least one processor; and at least one memory coupled to the at least one processor having stored thereon instructions which, when executed by the at least one processor, causes the at least one processor to perform operations comprising: accessing, from electronic storage, at least a first recording of a base script and a second recording of the base script, the base script defining operations executed in testing performance of a system; causing the system to execute the accessed first recording of the base script and the accessed second recording of the base script; storing, in electronic storage, a dynamic value list that describes dynamic values generated during execution of the accessed first recording of the base script and during execution of the accessed second recording of the base script; automatically, without human intervention, analyzing the stored dynamic value list to identify candidates for correlation within the base script; generating a correlated script based on the identified candidates for correlation and the base script; and storing, in electronic storage, the correlated script.

1. An automatic correlation accelerator tool comprising: at least one processor; and at least one memory coupled to the at least one processor having stored thereon instructions which, when executed by the at least one processor, causes the at least one processor to perform operations comprising: accessing, from electronic storage, at least a first recording of a base script and a second recording of the base script, the base script defining operations executed in testing performance of a system; causing the system to execute the accessed first recording of the base script and the accessed second recording of the base script; storing, in electronic storage, a dynamic value list that describes dynamic values generated during execution of the accessed first recording of the base script and during execution of the accessed second recording of the base script; automatically, without human intervention, analyzing the stored dynamic value list to identify candidates for correlation within the base script; generating a correlated script based on the identified candidates for correlation and the base script; and storing, in electronic storage, the correlated script. 6. The automatic correlation accelerator tool of claim 1 , wherein automatically analyzing the stored dynamic value list to identify candidates for correlation within the base script comprises automatically, without human intervention, generating a correlation log using the dynamic value list and the accessed first recording of the base script.

1. A system that facilitates handling a change associated with a database, comprising at least a processor executing the following components: an interface that receives data associated with a change to an object graph that is a cached view of the database; and a state transition logic component that maintains the change related to the object graph utilizing a context and a respective set of rules, wherein a rules component enforces the following set of rules to the object graph: 1) a detached object cannot be related to a non-detached object; and 2) a deleted object cannot be related to a non-deleted object, the context employs metadata to view the object graph with an abstraction of at least one of an entity or a relationship.

1. A system that facilitates handling a change associated with a database, comprising at least a processor executing the following components: an interface that receives data associated with a change to an object graph that is a cached view of the database; and a state transition logic component that maintains the change related to the object graph utilizing a context and a respective set of rules, wherein a rules component enforces the following set of rules to the object graph: 1) a detached object cannot be related to a non-detached object; and 2) a deleted object cannot be related to a non-deleted object, the context employs metadata to view the object graph with an abstraction of at least one of an entity or a relationship. 14. The system of claim 1 , the state transition logic component utilizes an object state transition with the following states: an added state; a detached state; a deleted state, an unchanged state; and a modified state.

17. The system of claim 11 , wherein the symbol list further comprise a suggested symbol list option comprising the new symbol name option where the new symbol name option is included in the symbol list.

17. The system of claim 11 , wherein the symbol list further comprise a suggested symbol list option comprising the new symbol name option where the new symbol name option is included in the symbol list. 19. The system of claim 17 , wherein the symbol list further comprises a suggested symbol list option description describing the suggested symbol list option.

31. A method of generating language comprising: parsing an input sentence into collocations; accessing with a processor a database of synonymous collocations generated using translation information; substituting parsed collocations in the input sentence with synonymous collocations from the database; and providing an output based at least in part on the synonymous collocations substituted for the parsed collocations in the input sentence.

31. A method of generating language comprising: parsing an input sentence into collocations; accessing with a processor a database of synonymous collocations generated using translation information; substituting parsed collocations in the input sentence with synonymous collocations from the database; and providing an output based at least in part on the synonymous collocations substituted for the parsed collocations in the input sentence. 32. The method of claim 31 wherein parsing includes parsing the input sentence in one language and wherein accessing includes accessing a database of synonymous collocations in a language different from the input language.

7. A method of projecting a representation of an Extensible Markup Language (XML) document, the method comprising steps of: A) receiving the XML document as input; B) receiving at least one XPath query to be evaluated against the XML document; C) deriving a set of XPath expressions from the XML document, wherein the XPath expressions are represented as a rooted XPath expression tree with labeled vertices and edges, and wherein the XPath expression tree comprises at least one axis selected from a group consisting of: backward, ancestor, parent, following, preceding, following-sibling, and preceding-sibling axes; and D) constructing a projection of the XML document for evaluation of the XPath query, the projection based on the XPath expression tree, wherein the constructing step comprises: 1) normalizing the XPath expression tree into a canonical form, wherein the normalizing step comprises: a) rewriting instances of following, preceding, following-sibling and preceding-sibling axes in the XPath expression tree into order-blind axes, such as parent and ancestor, by introducing new vertices such that there are no more instances of following and preceding in the rewritten XPath expression tree; and b) merging vertices of the rewritten XPath expression tree to remove redundancies; 2) traversing the XML document in a depth-first manner to build a tree representation of the XML document, the traversing step comprising: a) generating start events when the traversal first visits an element; b) generating end events once the traversal of a subtree rooted at that element is finished; c) concurrently with generating the events, constructing nodes for all elements that may participate in an embedding; and d) adding all ancestor nodes of the elements that may participate in an embedding; E) evaluating the at least one XPath query against the tree representation in a bottom-up manner to produce a result such that the result of the evaluation of the XPath query on the projection of the XML document is the same as a result of evaluation of the XPath query on the XML document and comprises all nodes that are solutions of the XPath query, and their backward axes; and F) serializing the result.

7. A method of projecting a representation of an Extensible Markup Language (XML) document, the method comprising steps of: A) receiving the XML document as input; B) receiving at least one XPath query to be evaluated against the XML document; C) deriving a set of XPath expressions from the XML document, wherein the XPath expressions are represented as a rooted XPath expression tree with labeled vertices and edges, and wherein the XPath expression tree comprises at least one axis selected from a group consisting of: backward, ancestor, parent, following, preceding, following-sibling, and preceding-sibling axes; and D) constructing a projection of the XML document for evaluation of the XPath query, the projection based on the XPath expression tree, wherein the constructing step comprises: 1) normalizing the XPath expression tree into a canonical form, wherein the normalizing step comprises: a) rewriting instances of following, preceding, following-sibling and preceding-sibling axes in the XPath expression tree into order-blind axes, such as parent and ancestor, by introducing new vertices such that there are no more instances of following and preceding in the rewritten XPath expression tree; and b) merging vertices of the rewritten XPath expression tree to remove redundancies; 2) traversing the XML document in a depth-first manner to build a tree representation of the XML document, the traversing step comprising: a) generating start events when the traversal first visits an element; b) generating end events once the traversal of a subtree rooted at that element is finished; c) concurrently with generating the events, constructing nodes for all elements that may participate in an embedding; and d) adding all ancestor nodes of the elements that may participate in an embedding; E) evaluating the at least one XPath query against the tree representation in a bottom-up manner to produce a result such that the result of the evaluation of the XPath query on the projection of the XML document is the same as a result of evaluation of the XPath query on the XML document and comprises all nodes that are solutions of the XPath query, and their backward axes; and F) serializing the result. 11. The method of claim 7 wherein the evaluating step comprises determining at the end element event, whether the element node should be created or not, based on current information.

1. An immediate, word-by-word speaking translator for speech handicapped persons and those speech handicapped having no sight, comprising a circuit having a pair of inputs, each feeding a separate code-data channel and code register,. said code-data channels utilizing a simplified machine language T=N.sup.X -1 WHERE X equals number of data channels; N=steps per channel; and T equals total number of words, said machine language being injected through code input impulses from said handicapped persons fed into a filter system, an automatic control channel and a voice synthesizer; whereby said code input pulses and automatic control channel adapt easily to physical disabilities of said handicapped persons.

1. An immediate, word-by-word speaking translator for speech handicapped persons and those speech handicapped having no sight, comprising a circuit having a pair of inputs, each feeding a separate code-data channel and code register,. said code-data channels utilizing a simplified machine language T=N.sup.X -1 WHERE X equals number of data channels; N=steps per channel; and T equals total number of words, said machine language being injected through code input impulses from said handicapped persons fed into a filter system, an automatic control channel and a voice synthesizer; whereby said code input pulses and automatic control channel adapt easily to physical disabilities of said handicapped persons. 9. A translator as recited in claim 1 and wherein said inputs are microphones energized from taps or directional audio clicks.

13. The computer implemented method of claim 11 , further comprising: providing an interface that presents at least portions of the notebook to the user via the at least one client computer; wherein the computer interface input of the user via the at least one client computer is via the interface to directly modify the content of the notebook of the user.

13. The computer implemented method of claim 11 , further comprising: providing an interface that presents at least portions of the notebook to the user via the at least one client computer; wherein the computer interface input of the user via the at least one client computer is via the interface to directly modify the content of the notebook of the user. 14. The computer implemented method of claim 13 , further comprising: providing a link that, upon activation, provides the interface that presents the portions of the notebook.

12. A system comprising: one or more computers and one or more storage devices storing instructions which are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: obtaining a training data set comprising at least one training example, the at least one training example being annotated with at least one training example natural language processing tag; adding a training example information retrieval model annotation to the at least one training example in the training data set to obtain an annotated training data set; training a natural language processing model on the annotated training data set to obtain a trained natural language processing model, wherein the operation of training comprises training the natural language processing model based on both the training example natural language processing tag and the training example information retrieval model annotation of the at least one training example; wherein training the natural language processing model on the annotated training data set comprises: extracting information retrieval features from the at least one training example in the annotated training data set based on the information retrieval model annotation, predicting part-of-speech tags for at least one word in the at least one training example, generating a confidence score for the predicted part-of-speech tags, and filtering the predicted part-of-speech tags if the confidence score is below a threshold; receiving a target document comprising text and at least one information retrieval model annotation; and generating, for at least one word in the text, a prediction and an additional confidence score for the prediction with the natural language processing model, wherein the operation of generating the prediction and the additional confidence score comprises using the information retrieval model annotation with the natural language processing model to generate the prediction.

12. A system comprising: one or more computers and one or more storage devices storing instructions which are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: obtaining a training data set comprising at least one training example, the at least one training example being annotated with at least one training example natural language processing tag; adding a training example information retrieval model annotation to the at least one training example in the training data set to obtain an annotated training data set; training a natural language processing model on the annotated training data set to obtain a trained natural language processing model, wherein the operation of training comprises training the natural language processing model based on both the training example natural language processing tag and the training example information retrieval model annotation of the at least one training example; wherein training the natural language processing model on the annotated training data set comprises: extracting information retrieval features from the at least one training example in the annotated training data set based on the information retrieval model annotation, predicting part-of-speech tags for at least one word in the at least one training example, generating a confidence score for the predicted part-of-speech tags, and filtering the predicted part-of-speech tags if the confidence score is below a threshold; receiving a target document comprising text and at least one information retrieval model annotation; and generating, for at least one word in the text, a prediction and an additional confidence score for the prediction with the natural language processing model, wherein the operation of generating the prediction and the additional confidence score comprises using the information retrieval model annotation with the natural language processing model to generate the prediction. 13. The system of claim 12 , wherein the target document is one of a search query and a potential search result.

15. A computer-implemented process comprising: processing user input to modify structure and content of an electronic document and to associated comments with the content of the electronic document, including presenting a graphical user interface including a document pane configured to display a currently viewed portion of the electronic document, wherein the graphical user interface further displays comments from a plurality of users in association with the currently viewed portion of the electronic document, wherein comments have at least context data indicating a location within the electronic document and content data; based on at least the currently viewed portion of the electronic document, identifying a next comment or previous comment associated with content of the electronic document outside of the currently viewed portion of the electronic document, and generating and displaying, with the displayed comments in the graphical user interface, a hint comprising a graphical representation of an indication of at least context data and content data of the identified comment; in response to user input to navigate to displaying a second portion of the electronic document as the currently viewed portion of the electronic document, repeating identifying, based on at least the currently viewed portion of the electronic document, a next or previous comment with respect to the second portion of the electronic document, and generating and displaying a hint of the identified next or previous comment; and in response to an input associated with a displayed hint related to a comment, navigating to displaying, in the document pane, a third portion of the electronic document associated with the comment related to the displayed hint.

15. A computer-implemented process comprising: processing user input to modify structure and content of an electronic document and to associated comments with the content of the electronic document, including presenting a graphical user interface including a document pane configured to display a currently viewed portion of the electronic document, wherein the graphical user interface further displays comments from a plurality of users in association with the currently viewed portion of the electronic document, wherein comments have at least context data indicating a location within the electronic document and content data; based on at least the currently viewed portion of the electronic document, identifying a next comment or previous comment associated with content of the electronic document outside of the currently viewed portion of the electronic document, and generating and displaying, with the displayed comments in the graphical user interface, a hint comprising a graphical representation of an indication of at least context data and content data of the identified comment; in response to user input to navigate to displaying a second portion of the electronic document as the currently viewed portion of the electronic document, repeating identifying, based on at least the currently viewed portion of the electronic document, a next or previous comment with respect to the second portion of the electronic document, and generating and displaying a hint of the identified next or previous comment; and in response to an input associated with a displayed hint related to a comment, navigating to displaying, in the document pane, a third portion of the electronic document associated with the comment related to the displayed hint. 16. The computer-implemented process of claim 15 , further comprising displaying only comments selected according to filter criteria.

1. A method of classifying a mood of a music file, comprising: extracting a Modified Discrete Cosine Transformation-based timbre feature from a compressed domain of a music file; extracting a Modified Discrete Cosine Transformation-based tempo feature from the compressed domain of the music file; and classifying a mood of the music file based on the extracted timbre feature and the extracted tempo feature, wherein the extracting the Modified Discrete Cosine Transformation-based timbre feature from the compressed domain of the music file comprises: extracting Modified Discrete Cosine Transformation coefficients by decoding a part of the music file; selecting the Modified Discrete Cosine Transformation coefficients of a predetermined number of sub-bands from the extracted Modified Discrete Cosine Transformation coefficients; and extracting a spectral centroid, a bandwidth, a rolloff, and a flux from the selected Modified Discrete Cosine Transformation coefficients, wherein the classifying the mood of the music file comprises: classifying a genre of the music file based on the extracted timbre feature; and reclassifying a category of the music file of the genre when uncertainty of a genre classification result is greater than a predetermined value; and wherein, in the reclassifying a category of the music file of the genre, the category of the music file of the genre is reclassified based on the extracted tempo feature.

1. A method of classifying a mood of a music file, comprising: extracting a Modified Discrete Cosine Transformation-based timbre feature from a compressed domain of a music file; extracting a Modified Discrete Cosine Transformation-based tempo feature from the compressed domain of the music file; and classifying a mood of the music file based on the extracted timbre feature and the extracted tempo feature, wherein the extracting the Modified Discrete Cosine Transformation-based timbre feature from the compressed domain of the music file comprises: extracting Modified Discrete Cosine Transformation coefficients by decoding a part of the music file; selecting the Modified Discrete Cosine Transformation coefficients of a predetermined number of sub-bands from the extracted Modified Discrete Cosine Transformation coefficients; and extracting a spectral centroid, a bandwidth, a rolloff, and a flux from the selected Modified Discrete Cosine Transformation coefficients, wherein the classifying the mood of the music file comprises: classifying a genre of the music file based on the extracted timbre feature; and reclassifying a category of the music file of the genre when uncertainty of a genre classification result is greater than a predetermined value; and wherein, in the reclassifying a category of the music file of the genre, the category of the music file of the genre is reclassified based on the extracted tempo feature. 7. The method of claim 1 , wherein, in the classifying the mood of the music file, the mood of the music file is classified into one of sad, calm, exciting, and pleasant.

8. A method comprising: tracking a location of a pen instrument when the pen instrument is used to modify a physical document, wherein sensors within the pen instrument track the location of the pen instrument; generating signals describing movement of the pen instrument based on the location of the pen instrument; transmitting the signals to a receiving device, wherein the signals are used to modify an electronic document corresponding to the physical document, and further wherein strokes made with the pen instrument are displayed when the electronic document is displayed.

8. A method comprising: tracking a location of a pen instrument when the pen instrument is used to modify a physical document, wherein sensors within the pen instrument track the location of the pen instrument; generating signals describing movement of the pen instrument based on the location of the pen instrument; transmitting the signals to a receiving device, wherein the signals are used to modify an electronic document corresponding to the physical document, and further wherein strokes made with the pen instrument are displayed when the electronic document is displayed. 13. The method of claim 8 wherein tracking a location of a pen instrument comprises generating an infrared signal with the pen instrument that is received by at least two infrared receiving scanning stations located at prescribed positions external to the pen instrument, the scanning stations to scan the area in which the pen instrument is used.

20. The system of claim 15 , wherein the required term of experience is rounded up to a unit of time.

20. The system of claim 15 , wherein the required term of experience is rounded up to a unit of time. 22. The system of claim 20 , wherein the unit of time is an integer.

1. A method of identifying whether two texts are parallel, bilingual texts, comprising: generating a query using a set of predetermined translation triggering words identified as being words that, when used in the query, are likely to return candidate texts for which a translation into the target language exists; executing the query over a network to return, as a search result, a candidate text, having a plurality of sentences, in a source language, that is related to the query, the query including words in the source language and including the translation triggering words provided to a search engine to search multiple data sources, the translation triggering words triggering the search engine to search for documents with translations; determining whether the candidate text includes a link to a linked text, having a plurality of sentences, in a target language; if the candidate text includes a link to a linked text in the target language, retrieving the linked text; determining whether the candidate text and the linked text are parallel, bilingual texts, in which the linked text is a translation of all of the plurality of sentences in the candidate text, using a parallel identification system to compare sentences in content of the candidate text with sentences in content of the linked text, line-by-line, to determine a degree of alignment between the candidate text and the linked text to obtain a degree of parallelism and to determine that the candidate text and the linked text are parallel, bilingual texts when the degree of parallelism meets a threshold; providing an output identifying the candidate text and the linked text as parallel, bilingual texts when the candidate text and the linked text are determined to be parallel, bilingual texts; and repeating the steps of generating a query, executing the query, determining whether the candidate text includes a link, retrieving, determining whether the candidate text and the linked text are parallel, and providing an output to generate a parallel, bilingual corpus of information that includes a plurality of separate, multi-sentence source documents in a source language along with a plurality of separate, multi-sentence target documents in the target language, the target documents being translations of the source documents.

1. A method of identifying whether two texts are parallel, bilingual texts, comprising: generating a query using a set of predetermined translation triggering words identified as being words that, when used in the query, are likely to return candidate texts for which a translation into the target language exists; executing the query over a network to return, as a search result, a candidate text, having a plurality of sentences, in a source language, that is related to the query, the query including words in the source language and including the translation triggering words provided to a search engine to search multiple data sources, the translation triggering words triggering the search engine to search for documents with translations; determining whether the candidate text includes a link to a linked text, having a plurality of sentences, in a target language; if the candidate text includes a link to a linked text in the target language, retrieving the linked text; determining whether the candidate text and the linked text are parallel, bilingual texts, in which the linked text is a translation of all of the plurality of sentences in the candidate text, using a parallel identification system to compare sentences in content of the candidate text with sentences in content of the linked text, line-by-line, to determine a degree of alignment between the candidate text and the linked text to obtain a degree of parallelism and to determine that the candidate text and the linked text are parallel, bilingual texts when the degree of parallelism meets a threshold; providing an output identifying the candidate text and the linked text as parallel, bilingual texts when the candidate text and the linked text are determined to be parallel, bilingual texts; and repeating the steps of generating a query, executing the query, determining whether the candidate text includes a link, retrieving, determining whether the candidate text and the linked text are parallel, and providing an output to generate a parallel, bilingual corpus of information that includes a plurality of separate, multi-sentence source documents in a source language along with a plurality of separate, multi-sentence target documents in the target language, the target documents being translations of the source documents. 2. The method of claim 1 and further comprising: if the candidate text does not include a link or if the linked text and the candidate text are not parallel, bilingual texts, determining whether the candidate text includes one or more words in the target language; if so, extracting the one or more words in the target language from the candidate text; and executing a query over the network wherein the query includes the words in the target language.

11. The method for resolving natural language ambiguities within text documents of claim 1 , further comprising determining discourse categories of said text documents using a discourse category analysis module whereby additional contextual features are extracted.

11. The method for resolving natural language ambiguities within text documents of claim 1 , further comprising determining discourse categories of said text documents using a discourse category analysis module whereby additional contextual features are extracted. 12. The method for determining discourse categories of claim 11 , comprising the following steps of: training probabilistic discourse category classifiers using annotated training data containing discourse categories for each document; determining discourse categories of said text documents by maximizing the probability computed using said probabilistic discourse category classifiers based on contextual features; and integrating additional contextual features as generated by one or more of the following natural language processing modules into said probabilistic classifiers whereby said measure of confidence is improved: using a word sense disambiguation module to determine word senses and the associated measure of confidence for each word; using a chunking module to identify multi-word phrases and the associated measure of confidence for each phrase; using a named-entity recognition module to identify named entities and the associated measure of confidence for each entity; using a syntactic parsing module to construct sentential parse trees and the associated measure of confidence for each tree; using an anaphora resolution module to identify anaphor references and the associated measure of confidence for each reference; using a discourse structure analysis module to determine discourse structures and the associated measure of confidence for each structure.

13. A cloud storage system, comprising: a communication circuitry in communication with: a file database circuitry for storing a plurality of files and metadata associated with each of the plurality of files; and a user database circuitry for storing information associating at least one user with at least one file, wherein the communication circuitry is configured to: receive a request, from a user, to edit metadata associated with a file in the plurality of files; identify a first application generates the request; divide the metadata associated with the file into a first set of metadata categories and a second set of metadata categories, wherein the first set of metadata categories is associated with the first application and the second set of metadata categories is not associated with the first application; filter the first set of metadata categories to obtain filtered metadata containing metadata associated with the file; provide the filtered metadata to the first application; receive edits from the user to metadata associated with the first file, wherein the edited metadata is in a metadata category in the first set of metadata categories; and determine the category of metadata of the received edits and: in response to determining that the received edits belong to the first set of metadata categories, apply the edits to the metadata category, wherein the received edits belong to the first set of metadata categories; and in response to determining that the category of the metadata does not exist, generate a new metadata category associated only with the application in the first set of metadata categories.

13. A cloud storage system, comprising: a communication circuitry in communication with: a file database circuitry for storing a plurality of files and metadata associated with each of the plurality of files; and a user database circuitry for storing information associating at least one user with at least one file, wherein the communication circuitry is configured to: receive a request, from a user, to edit metadata associated with a file in the plurality of files; identify a first application generates the request; divide the metadata associated with the file into a first set of metadata categories and a second set of metadata categories, wherein the first set of metadata categories is associated with the first application and the second set of metadata categories is not associated with the first application; filter the first set of metadata categories to obtain filtered metadata containing metadata associated with the file; provide the filtered metadata to the first application; receive edits from the user to metadata associated with the first file, wherein the edited metadata is in a metadata category in the first set of metadata categories; and determine the category of metadata of the received edits and: in response to determining that the received edits belong to the first set of metadata categories, apply the edits to the metadata category, wherein the received edits belong to the first set of metadata categories; and in response to determining that the category of the metadata does not exist, generate a new metadata category associated only with the application in the first set of metadata categories. 27. The system of claim 13 , wherein: the file database is further configured for storing a folder associated with one or more files and metadata associated with the folder, and the communication processor is further configured to edit one or more metadata categories based on metadata associated with the folder.

8. The method of claim 1 , wherein the expression that operates on the XML construct is an XML-specific operator.

8. The method of claim 1 , wherein the expression that operates on the XML construct is an XML-specific operator. 9. A volatile or non-volatile machine-readable storage medium storing one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 8 .

1. One or more computer-readable media storing computer-executable instructions that, when executed, cause one or more processors to perform acts comprising: providing a first input mode that restricts display of an input into a text field to a first set of characters; causing display of one or more characters of the first set of characters within the text field in a first manner; providing a second input mode that restricts display of an input into the text field to a second set of characters that differs from the first set of characters; and causing display of one or more characters of the second set of characters within the text field in a second manner that differs from the first manner.

1. One or more computer-readable media storing computer-executable instructions that, when executed, cause one or more processors to perform acts comprising: providing a first input mode that restricts display of an input into a text field to a first set of characters; causing display of one or more characters of the first set of characters within the text field in a first manner; providing a second input mode that restricts display of an input into the text field to a second set of characters that differs from the first set of characters; and causing display of one or more characters of the second set of characters within the text field in a second manner that differs from the first manner. 7. One or more computer-readable media as recited in claim 1 , wherein the first set of characters and the second set of characters comprise mutually exclusive sets of characters, and wherein the acts further comprise: receiving an input specifying a predefined character; receiving an input specifying a character subsequent to the received predefined character; at least partly in response to determining that the character subsequent to the received predefined character comprises a character of the first set of characters, providing the first input mode; and at least partly in response to determining that the character subsequent to the received predefined character comprises a character of the second set of characters, providing the second input mode.

1. A method for ranking unlabeled matches, each of the unlabeled matches existing between a first member of a dating website and a corresponding one of a first set of members of the dating website, the method comprising: transmitting, by a first mobile device via a web server, messages and profile views directed toward one of a second set of members of the dating website, the first mobile device being associated with the first member of the dating website; transmitting, by each of a plurality of mobile devices via the web server, messages and profile views directed toward the first member of the dating website, each of the plurality of mobile devices being associated with a corresponding one of the second set of members of the dating website; receiving, by the web server, a plurality of labeled matches, wherein each of the plurality of labeled matches exists between the first member of the dating website and the corresponding one of the second set of members of the dating website, and wherein each of the plurality of labeled matches are labeled based on a dating profile of the first member of the dating website, a dating profile of the corresponding one of the second set of members of the dating website, and behavioral features, the behavioral features comprising: a first density of profile views initiated by the first member of the dating website toward the corresponding one of the second set of members of the dating website, the first density of profile views being measured within a time period between a first message and a last message exchanged between the first member of the dating website and the corresponding one of the second set of members of the dating website, a second density of profile views initiated by the corresponding one of the second set of members of the dating website toward the first member of the dating website, the second density of profile views being measured within the time period, a message disparity that identifies a difference between a number of messages sent by the first member of the dating website and a number of messages sent by the corresponding one of the second set of members of the dating website, a number of times that the first member of the dating website viewed the dating profile of the corresponding one of the second set of members of the dating website, a number of times that the corresponding one of the second set of members of the dating website viewed the dating profile of the first member of the dating website, and whether the first member of the dating website and the corresponding one of the second set of members of the dating website exchange phone numbers with one another; determining, by the web server, a probability of relevance of each of the plurality of labeled matches based on the behavioral features, wherein: the probability of relevance increases as a first difference decreases and the probability of relevance decreases as the first difference increases, the first difference being between the first density of profile views initiated by the first member of the dating website toward the corresponding one of the second set of members of the dating website within the time period and the second density of profile views initiated by the corresponding one of the second set of members of the dating website toward the first member of the dating website within the time period, the probability of relevance increases as the message disparity decreases and the probability of relevance decreases as the message disparity increases, the probability of relevance increases as a second difference decreases and the probability of relevance decreases as the second difference increases, the second difference being between the number of times that the first member of the dating website viewed the dating profile of the corresponding one of the second set of members of the dating website and the number of times that the corresponding one of the second set of members of the dating website viewed the dating profile of the first member of the dating website, and ones of the plurality of labeled matches where the first member of the dating website and the corresponding one of the second set of members of the dating website exchange phone numbers with one another are assigned a higher probability of relevance than other ones of the plurality of labeled matches where the first member of the dating website and the corresponding one of the second set of members of the dating website do not exchange phone numbers with one another; for each of the plurality of labeled matches: comparing, by a processor in the web server on an attribute-by-attribute basis, first binary data in each attribute of the dating profile of the first member of the dating website to second binary data in a corresponding attribute of the dating profile of the corresponding one of the second set of members of the dating website to produce ranking features of each of the plurality of labeled matches, and correlating, by the processor in the web server, the ranking features of each of the plurality of labeled matches to the probability of relevance of each of the plurality of labeled matches to produce a ranking function; training, by the web server, boosted regression trees based on the probability of relevance of each of the plurality of labeled matches, the behavioral features, the ranking features of each of the plurality of labeled matches, the ranking function, the dating profile of the first member of the dating website, and the dating profile of the corresponding one of the second set of members of the dating website, wherein, upon completion of the training, the boosted regression trees are configured to utilize, as input, ranking features observed from a given unlabeled match and generate, as output, a probability of relevance of the given unlabeled match; determining, by the processor in the web server utilizing the boosted regression trees, a probability of relevance of each of the unlabeled matches by: comparing, on an attribute-by-attribute basis, the first binary data in each attribute of the dating profile of the first member of the dating website to third binary data in a corresponding attribute of a dating profile of the corresponding one of the first set of members of the dating website to produce ranking features of each of the unlabeled matches, wherein each of the unlabeled matches lack the behavioral features, and calculating the probability of relevance each of the unlabeled matches by inputting, into the ranking function, the ranking features of each of the unlabeled matches and retrieving, from the ranking function, the probability of relevance each of the unlabeled matches, wherein the ranking features of each of the unlabeled matches are used as a proxy for the behavioral features; calculating, by the web server, a rank for each of the unlabeled matches based on the probability of relevance of each of the unlabeled matches to generate a set of ranked matches; and transmitting, by the web server over a network interface, at least a portion of the set of ranked matches to the first mobile device associated with the first member of the dating website.

1. A method for ranking unlabeled matches, each of the unlabeled matches existing between a first member of a dating website and a corresponding one of a first set of members of the dating website, the method comprising: transmitting, by a first mobile device via a web server, messages and profile views directed toward one of a second set of members of the dating website, the first mobile device being associated with the first member of the dating website; transmitting, by each of a plurality of mobile devices via the web server, messages and profile views directed toward the first member of the dating website, each of the plurality of mobile devices being associated with a corresponding one of the second set of members of the dating website; receiving, by the web server, a plurality of labeled matches, wherein each of the plurality of labeled matches exists between the first member of the dating website and the corresponding one of the second set of members of the dating website, and wherein each of the plurality of labeled matches are labeled based on a dating profile of the first member of the dating website, a dating profile of the corresponding one of the second set of members of the dating website, and behavioral features, the behavioral features comprising: a first density of profile views initiated by the first member of the dating website toward the corresponding one of the second set of members of the dating website, the first density of profile views being measured within a time period between a first message and a last message exchanged between the first member of the dating website and the corresponding one of the second set of members of the dating website, a second density of profile views initiated by the corresponding one of the second set of members of the dating website toward the first member of the dating website, the second density of profile views being measured within the time period, a message disparity that identifies a difference between a number of messages sent by the first member of the dating website and a number of messages sent by the corresponding one of the second set of members of the dating website, a number of times that the first member of the dating website viewed the dating profile of the corresponding one of the second set of members of the dating website, a number of times that the corresponding one of the second set of members of the dating website viewed the dating profile of the first member of the dating website, and whether the first member of the dating website and the corresponding one of the second set of members of the dating website exchange phone numbers with one another; determining, by the web server, a probability of relevance of each of the plurality of labeled matches based on the behavioral features, wherein: the probability of relevance increases as a first difference decreases and the probability of relevance decreases as the first difference increases, the first difference being between the first density of profile views initiated by the first member of the dating website toward the corresponding one of the second set of members of the dating website within the time period and the second density of profile views initiated by the corresponding one of the second set of members of the dating website toward the first member of the dating website within the time period, the probability of relevance increases as the message disparity decreases and the probability of relevance decreases as the message disparity increases, the probability of relevance increases as a second difference decreases and the probability of relevance decreases as the second difference increases, the second difference being between the number of times that the first member of the dating website viewed the dating profile of the corresponding one of the second set of members of the dating website and the number of times that the corresponding one of the second set of members of the dating website viewed the dating profile of the first member of the dating website, and ones of the plurality of labeled matches where the first member of the dating website and the corresponding one of the second set of members of the dating website exchange phone numbers with one another are assigned a higher probability of relevance than other ones of the plurality of labeled matches where the first member of the dating website and the corresponding one of the second set of members of the dating website do not exchange phone numbers with one another; for each of the plurality of labeled matches: comparing, by a processor in the web server on an attribute-by-attribute basis, first binary data in each attribute of the dating profile of the first member of the dating website to second binary data in a corresponding attribute of the dating profile of the corresponding one of the second set of members of the dating website to produce ranking features of each of the plurality of labeled matches, and correlating, by the processor in the web server, the ranking features of each of the plurality of labeled matches to the probability of relevance of each of the plurality of labeled matches to produce a ranking function; training, by the web server, boosted regression trees based on the probability of relevance of each of the plurality of labeled matches, the behavioral features, the ranking features of each of the plurality of labeled matches, the ranking function, the dating profile of the first member of the dating website, and the dating profile of the corresponding one of the second set of members of the dating website, wherein, upon completion of the training, the boosted regression trees are configured to utilize, as input, ranking features observed from a given unlabeled match and generate, as output, a probability of relevance of the given unlabeled match; determining, by the processor in the web server utilizing the boosted regression trees, a probability of relevance of each of the unlabeled matches by: comparing, on an attribute-by-attribute basis, the first binary data in each attribute of the dating profile of the first member of the dating website to third binary data in a corresponding attribute of a dating profile of the corresponding one of the first set of members of the dating website to produce ranking features of each of the unlabeled matches, wherein each of the unlabeled matches lack the behavioral features, and calculating the probability of relevance each of the unlabeled matches by inputting, into the ranking function, the ranking features of each of the unlabeled matches and retrieving, from the ranking function, the probability of relevance each of the unlabeled matches, wherein the ranking features of each of the unlabeled matches are used as a proxy for the behavioral features; calculating, by the web server, a rank for each of the unlabeled matches based on the probability of relevance of each of the unlabeled matches to generate a set of ranked matches; and transmitting, by the web server over a network interface, at least a portion of the set of ranked matches to the first mobile device associated with the first member of the dating website. 5. The method as recited in claim 1 , wherein the behavioral features further comprise at least one selected from a group consisting of: 1) the first member of the dating website viewed the dating profile of the corresponding one of the second set of members of the dating website but did not send a message to the corresponding one of the second set of members of the dating website; and 2) the corresponding one of the second set of members of the dating website viewed the dating profile of the first member of the dating website but did not send a message to the first member of the dating website.

5. The computer-implemented method of claim 3 , wherein inserting the search result identifying the second resource in the set of search results identifying the first resources comprises: determining an insertion score based, in part, on the search probability ratio, wherein the insertion score defines an ordinal insertion position at which a second resource search result referencing the second resource is to be inserted into a ranking of first resource search results referencing the first resources; and generating a search results resource for displaying the first resource search results according to their respective ordinal positions in the ranking and the second resource search results at the ordinal insertion position.

5. The computer-implemented method of claim 3 , wherein inserting the search result identifying the second resource in the set of search results identifying the first resources comprises: determining an insertion score based, in part, on the search probability ratio, wherein the insertion score defines an ordinal insertion position at which a second resource search result referencing the second resource is to be inserted into a ranking of first resource search results referencing the first resources; and generating a search results resource for displaying the first resource search results according to their respective ordinal positions in the ranking and the second resource search results at the ordinal insertion position. 6. The computer-implemented method of claim 5 , wherein determining an insertion score based, in part, on the search probability ratio comprises: determining an insertion score corresponding to a first ordinal position when the search probability ratio meets a first insertion threshold; determining an insertion score corresponding to a second ordinal position when the search probability ratio meets a second insertion threshold but does not meet the first insertion threshold; and determining an insertion score corresponding to a third ordinal position when the search probability ratio meets a third insertion threshold but does not meet the second insertion threshold.

10. A computer software product, including a non-transitory computer-readable storage medium in which computer program instructions are stored, which instructions, when executed by a computer, cause the computer to perform the steps of: identifying properties of a target individual, wherein a correlation processor extracts user identifiers from retrieved data items, and correlates the user identifiers from different web sites; building an initial social circle of the target individual by crawling a plurality of web sites from different social media providers to identify direct and indirect associations between users of the social media providers and the target individual, wherein the target individual has no direct connection with at least one of the social media providers; deriving references to the target individual from the direct and indirect associations; and compiling a dossier on the target individual from the references to the target individual.

10. A computer software product, including a non-transitory computer-readable storage medium in which computer program instructions are stored, which instructions, when executed by a computer, cause the computer to perform the steps of: identifying properties of a target individual, wherein a correlation processor extracts user identifiers from retrieved data items, and correlates the user identifiers from different web sites; building an initial social circle of the target individual by crawling a plurality of web sites from different social media providers to identify direct and indirect associations between users of the social media providers and the target individual, wherein the target individual has no direct connection with at least one of the social media providers; deriving references to the target individual from the direct and indirect associations; and compiling a dossier on the target individual from the references to the target individual. 11. The computer software product according to claim 10 , wherein the instructions cause the computer to perform the additional steps of: expanding the initial social circle by building respective new social circles having new associations by crawling the plurality of web sites; and deriving additional references to the target individual from the new associations in the new social circles.

2. The method of claim 1 , wherein the first score, the second score, and the third score are combined via an aggregation function.

2. The method of claim 1 , wherein the first score, the second score, and the third score are combined via an aggregation function. 3. The method of claim 2 , wherein the windowing system event sequences modality is configured to categorize behavior of the user in the windowing system based operating system on the computer; and wherein categorizing the behavior of the user includes determining content selection strategies, determining application and desktop navigation strategies, determining text editing strategies, and determining context sensitive pop-up menus strategies.

1. A method for detection of signature marks in a document, comprising: selecting candidate text objects for each of an ordered set of optical character recognition (OCR) processed document pages; providing for identifying sequences of elements in the candidate text objects, each detected element of an identified sequence occurring on a different page of the document, the sequence having a numbering pattern including an incremental part and optionally a fixed part, missing elements between two detected elements of the sequence being permitted; for an identified sequence: generating a model of the sequence, the model including: the numbering pattern of the sequence, an increment, which is computed based on the distance between pages on which consecutive elements of the sequence are identified, a valid sequence having an increment of greater than 1, and a first page, which corresponds to a page of the document on which the sequence starts; validating the sequence with the model; and for a valid sequence, identifying elements of the sequence in the pages of the document as signature marks.

1. A method for detection of signature marks in a document, comprising: selecting candidate text objects for each of an ordered set of optical character recognition (OCR) processed document pages; providing for identifying sequences of elements in the candidate text objects, each detected element of an identified sequence occurring on a different page of the document, the sequence having a numbering pattern including an incremental part and optionally a fixed part, missing elements between two detected elements of the sequence being permitted; for an identified sequence: generating a model of the sequence, the model including: the numbering pattern of the sequence, an increment, which is computed based on the distance between pages on which consecutive elements of the sequence are identified, a valid sequence having an increment of greater than 1, and a first page, which corresponds to a page of the document on which the sequence starts; validating the sequence with the model; and for a valid sequence, identifying elements of the sequence in the pages of the document as signature marks. 4. The method of claim 1 , wherein the selecting of the candidate text objects comprises selecting the last n text objects on each page, provided that these text objects are within a predetermined area of the page, and where n is a predetermined number.

1. A computer-implemented method comprising: extracting product descriptors from a product page; receiving a result of a search for prices of the product based on the extracted product descriptors; generating, using the result of the search for prices of the product, a price comparison result for the product displayed in the product page; identifying, using the price comparison result, a product with selected attributes; generating a buyer button, the buyer button including a direct link to the identified product with selected attributes; generating a browser toolbar and the product page contemporaneously in a client browser, wherein the browser toolbar includes a seller button, the buyer button, and the price comparison result for the product displayed in the product page; and generating, in response to operation of the seller button, a product listing on a marketplace, wherein generating the product listing includes determining a product category and a product description from the product descriptors extracted from the product page; wherein the preceding steps are performed by a computer processor.

1. A computer-implemented method comprising: extracting product descriptors from a product page; receiving a result of a search for prices of the product based on the extracted product descriptors; generating, using the result of the search for prices of the product, a price comparison result for the product displayed in the product page; identifying, using the price comparison result, a product with selected attributes; generating a buyer button, the buyer button including a direct link to the identified product with selected attributes; generating a browser toolbar and the product page contemporaneously in a client browser, wherein the browser toolbar includes a seller button, the buyer button, and the price comparison result for the product displayed in the product page; and generating, in response to operation of the seller button, a product listing on a marketplace, wherein generating the product listing includes determining a product category and a product description from the product descriptors extracted from the product page; wherein the preceding steps are performed by a computer processor. 10. The computer-implemented method of claim 1 , wherein extracting product descriptors from the product page includes extracting product descriptors from a page title of the product page.

13. The method as recited in claim 1 , further comprising: assigning, by the processor, a first set of scores for the first set of keywords based on the first set of numbers; assigning, by the processor, a second set of scores for the first set of keywords based on the second set of numbers; and wherein determining the respective combination number is based on the first and second set of scores.

13. The method as recited in claim 1 , further comprising: assigning, by the processor, a first set of scores for the first set of keywords based on the first set of numbers; assigning, by the processor, a second set of scores for the first set of keywords based on the second set of numbers; and wherein determining the respective combination number is based on the first and second set of scores. 14. The method as recited in claim 13 , wherein: the metric rules relate to weights for the first and second sets of scores.

15. The method according to claim 2 , further comprising: in the aggregating step, aggregating annotations by category contexts such that an annotation corresponds to a category context if it contains the keywords of the category context; determining category contexts that have a predefined minimum number of unique user identifiers and a predefined minimum number of unique item identifiers in their aggregated annotations; and clustering items and users simultaneously by category context such that the unique user identifiers for a determined category context is identified as a cluster of users and the unique item identifiers for the same determined category context is identified as a cluster of items.

15. The method according to claim 2 , further comprising: in the aggregating step, aggregating annotations by category contexts such that an annotation corresponds to a category context if it contains the keywords of the category context; determining category contexts that have a predefined minimum number of unique user identifiers and a predefined minimum number of unique item identifiers in their aggregated annotations; and clustering items and users simultaneously by category context such that the unique user identifiers for a determined category context is identified as a cluster of users and the unique item identifiers for the same determined category context is identified as a cluster of items. 21. The method according to claim 15 , further comprising: identifying a subset of the determined category contexts such that the category contexts of the identified subset have more people in its cluster of users relative to the determined category contexts as a whole and have fewer items in its cluster of items relative to the determined category contexts as a whole; and presenting the identified subset of category contexts as hot topics.

1. A circuit comprising: a first cache configured to store translations between address domains, the first cache including first and second logical portions, the first logical portion configured to store translations between a first address domain and a second address domain, the second logical portion configured to store translations between the second address domain and a third address domain; a second cache configured to store translations between the first address domain and the third address domain based on entries in the first cache; a third cache configured to store tags associated with the translations of the first and second cache; and a processor configured to 1) write an entry to the third cache, the entry including a subset of fields populated from a corresponding translation stored at the second cache, and 2) detect a deleted entry in at least one of the first logical portion and the second logical portion and invalidate corresponding entries in the second and third caches; wherein the processor is further configured, in response to detecting an absence of a matching entry in the second cache, to match the address request against the first cache, the address result corresponding to an entry in the first cache.

1. A circuit comprising: a first cache configured to store translations between address domains, the first cache including first and second logical portions, the first logical portion configured to store translations between a first address domain and a second address domain, the second logical portion configured to store translations between the second address domain and a third address domain; a second cache configured to store translations between the first address domain and the third address domain based on entries in the first cache; a third cache configured to store tags associated with the translations of the first and second cache; and a processor configured to 1) write an entry to the third cache, the entry including a subset of fields populated from a corresponding translation stored at the second cache, and 2) detect a deleted entry in at least one of the first logical portion and the second logical portion and invalidate corresponding entries in the second and third caches; wherein the processor is further configured, in response to detecting an absence of a matching entry in the second cache, to match the address request against the first cache, the address result corresponding to an entry in the first cache. 2. The circuit of claim 1 , wherein the processor is further configured to match an address request against the second cache and output a corresponding address result.

7. An electronic dictionary for providing word information in response to actuation of the dictionary, comprising: first memory means for storing a plurality of words therein; said first memory means including a plurality of first storage areas each corresponding to a predetermined set of words each of said first storage areas being divided into a plurality of second storage areas each corresponding to a predetermined subset of words, greater than one, wherein the predetermined subset of words in a second storage area is less than the predetermined set of words in a first storage area; first selection means for selecting said plurality of first storage areas; second selection means for selecting said plurality of second storage areas; access means, responsive to the actuation of said first and second selection means, for accessing any one of said predetermined sets of words in said plurality of first storage areas, and for accessing any one of said predetermined subsets of words in said plurality of second storage areas, said access means including means for displaying the first and last words of each set or subset upon actuation of said first or second selection means respectively; and means for retrieving an entry word from any one of said predetermined sets of words.

7. An electronic dictionary for providing word information in response to actuation of the dictionary, comprising: first memory means for storing a plurality of words therein; said first memory means including a plurality of first storage areas each corresponding to a predetermined set of words each of said first storage areas being divided into a plurality of second storage areas each corresponding to a predetermined subset of words, greater than one, wherein the predetermined subset of words in a second storage area is less than the predetermined set of words in a first storage area; first selection means for selecting said plurality of first storage areas; second selection means for selecting said plurality of second storage areas; access means, responsive to the actuation of said first and second selection means, for accessing any one of said predetermined sets of words in said plurality of first storage areas, and for accessing any one of said predetermined subsets of words in said plurality of second storage areas, said access means including means for displaying the first and last words of each set or subset upon actuation of said first or second selection means respectively; and means for retrieving an entry word from any one of said predetermined sets of words. 12. A dictionary according to claim 7, wherein said first and second selection means include a backward search means for sequentially reversing through said first and second storage areas set by set and subset by subset respectively, and for sequentially reversing through one of said plurality of selected sets or subsets word by word.

1. A method comprising: for each dialect in a plurality of dialects identified within a speech utterance, selecting, via a processor, a corresponding dialect grammar, to yield a plurality of dialect grammars; blending, via the processor, the plurality of dialect grammars, to yield a blended dialect grammar; and recognizing the speech utterance using the blended dialect grammar.

1. A method comprising: for each dialect in a plurality of dialects identified within a speech utterance, selecting, via a processor, a corresponding dialect grammar, to yield a plurality of dialect grammars; blending, via the processor, the plurality of dialect grammars, to yield a blended dialect grammar; and recognizing the speech utterance using the blended dialect grammar. 5. The method of claim 1 , wherein selection of the corresponding dialect grammar is based on a user location.

1. A phone mail terminal, comprising: a housing having a paper input and a telephone line connection; a sensor in said housing and arranged to sense application of a paper document at the paper input, said sensor being operable upon sensing the paper document to output a document insertion signal; a scanner in the housing, the scanner being responsive to the document insertion signal so as to scan the paper document and to generate an electronic image of the paper document as an electronic document; and, a transmitter in the housing, the transmitter being connected to the scanner to receive the electronic image and being operable to automatically transmit the electronic document telephonically over a telephone line to a predetermined telephone number without user in put of the telephone number to the phone mail terminal.

1. A phone mail terminal, comprising: a housing having a paper input and a telephone line connection; a sensor in said housing and arranged to sense application of a paper document at the paper input, said sensor being operable upon sensing the paper document to output a document insertion signal; a scanner in the housing, the scanner being responsive to the document insertion signal so as to scan the paper document and to generate an electronic image of the paper document as an electronic document; and, a transmitter in the housing, the transmitter being connected to the scanner to receive the electronic image and being operable to automatically transmit the electronic document telephonically over a telephone line to a predetermined telephone number without user in put of the telephone number to the phone mail terminal. 17. The phone mail terminal of claim 1 further comprising a status indicating light to indicate the status of the phone mail terminal.

15. A non-transitory computer readable medium containing computer executable instructions that, when executed by one or more processors of a computer, allow said one of more processors to execute the following steps: providing at least a closed extract stored in said computer, wherein said closed extract is a data set that contains references to the words of a language sample of a target language, and optionally also contains references to additional words or localizers, wherein a localizer is a string of symbols, said string of symbols not being a word in said target language, providing at least two relations stored in said computer, wherein a relation is a data set that comprises the information required to perform one or more modifications upon said closed extract, activating at least one of said relations, wherein said activation applies to said closed extract the one or more modification that are associated to said one relation, said application producing a second closed extract, this second closed extract being a modified closed extract, filtering at least one of said closed extracts to create an open extract, said open extract being a text fragment in the target language optionally containing in addition one or more localizers, wherein an open extract that is produced after filtering a modified closed extract is a modified version of said sample, presenting said open extract in said display, wherein said modified version differs from said sample in one of the ways included in the following plurality of ways: it contains additional words, or it contains some of the same words in different order, or it contains localizers, so that presenting one or more modified versions helps the user to understand said sample, and said method provides an aid to the user for understanding said sample.

15. A non-transitory computer readable medium containing computer executable instructions that, when executed by one or more processors of a computer, allow said one of more processors to execute the following steps: providing at least a closed extract stored in said computer, wherein said closed extract is a data set that contains references to the words of a language sample of a target language, and optionally also contains references to additional words or localizers, wherein a localizer is a string of symbols, said string of symbols not being a word in said target language, providing at least two relations stored in said computer, wherein a relation is a data set that comprises the information required to perform one or more modifications upon said closed extract, activating at least one of said relations, wherein said activation applies to said closed extract the one or more modification that are associated to said one relation, said application producing a second closed extract, this second closed extract being a modified closed extract, filtering at least one of said closed extracts to create an open extract, said open extract being a text fragment in the target language optionally containing in addition one or more localizers, wherein an open extract that is produced after filtering a modified closed extract is a modified version of said sample, presenting said open extract in said display, wherein said modified version differs from said sample in one of the ways included in the following plurality of ways: it contains additional words, or it contains some of the same words in different order, or it contains localizers, so that presenting one or more modified versions helps the user to understand said sample, and said method provides an aid to the user for understanding said sample. 17. A non-transitory computer readable medium as claimed in claim 15 , wherein said computer executable instructions allow said one or more processor to further perform the following steps: providing a list of modification types, wherein a modification type is a data entity that defines what modification would be performed upon one or more fragments of closed extracts, providing at least one relational scheme, wherein a relational scheme is a data set that performs as a template, said relational scheme containing at least two parts, the first one of said parts defining one of said modification types, and the second one of said parts being a variable that can be assigned to one fragment of said closed extracts, presenting said relational scheme in said display, detecting a user action, said action assigning a specific fragment of said closed extract to said variable, and creating a new relation, said creation assigning said relational scheme and said fragment to said relation, so that this steps provides an aid to the user to create new relations.

27. The at least one computer-readable medium of claim 25 , wherein the at least one sentence form comprises at least one class, and wherein the annotation information is derived based on an attribute relating to the at least one class.

27. The at least one computer-readable medium of claim 25 , wherein the at least one sentence form comprises at least one class, and wherein the annotation information is derived based on an attribute relating to the at least one class. 28. The at least one computer-readable medium of claim 27 , wherein the annotation information indicates allowable values of the attribute.

8. A system for facilitating management of risk related to political exposure associated with a financial transaction, comprising: a processor; computer-readable instructions that program the processor to: receive digital financial transaction data associated with the transaction; determine that a participant associated with the financial transaction is a politically identified person (“PIP”) by: comparing data identifying the participant associated with the financial transaction data to information stored in a database, wherein comparing comprises searching for both identical and similar names in the database; and verifying the data identifying the participant by comparing the financial transaction data to information associated with the data identifying the participant stored in the database if the data identifying the participant matches a name in the database; wherein verifying the data identifying the participant further comprises determining a certainty of the match by scoring the comparisons between the financial transaction data and the information associated with the data identifying the participant; calculate an overall transaction political risk quotient associated with the financial transaction; generate based on the overall transaction political risk quotient, a suggested action for the financial transaction.

8. A system for facilitating management of risk related to political exposure associated with a financial transaction, comprising: a processor; computer-readable instructions that program the processor to: receive digital financial transaction data associated with the transaction; determine that a participant associated with the financial transaction is a politically identified person (“PIP”) by: comparing data identifying the participant associated with the financial transaction data to information stored in a database, wherein comparing comprises searching for both identical and similar names in the database; and verifying the data identifying the participant by comparing the financial transaction data to information associated with the data identifying the participant stored in the database if the data identifying the participant matches a name in the database; wherein verifying the data identifying the participant further comprises determining a certainty of the match by scoring the comparisons between the financial transaction data and the information associated with the data identifying the participant; calculate an overall transaction political risk quotient associated with the financial transaction; generate based on the overall transaction political risk quotient, a suggested action for the financial transaction. 13. The system of claim 8 , wherein the financial transaction is at least one of: (i) a request to open a new account; and (ii) a transaction associated with an existing account.

9. A system for accessing requested data in a database on a computer readable storage medium using beans, comprising: a database that stores data on a computer readable storage medium; and an application server that includes logic for allowing a client to invoke a single bean select method of a bean query language with a query for requested data from a database, wherein the requested data includes data from multiple fields of the database corresponding to multiple bean types; translating the bean select method of the bean query language into a database language query and forwarding the query to the database in a database language; receiving the results of the query at the database to retrieve the requested data, including data from multiple fields of the database corresponding to multiple bean types; populating a single result set object in the bean query language with the requested data from multiple fields of the database corresponding to multiple bean types into the single result set object, wherein storing the data bypasses the steps of creating separate bean instances for the requested data and invoking individual net methods to retrieve multiple data fields, and wherein the result set object retains the relationships between the data corresponding to the original database schema; and providing access to the requested data in the result set object, wherein the result set object contains data from multiple fields of the database corresponding to multiple bean types.

9. A system for accessing requested data in a database on a computer readable storage medium using beans, comprising: a database that stores data on a computer readable storage medium; and an application server that includes logic for allowing a client to invoke a single bean select method of a bean query language with a query for requested data from a database, wherein the requested data includes data from multiple fields of the database corresponding to multiple bean types; translating the bean select method of the bean query language into a database language query and forwarding the query to the database in a database language; receiving the results of the query at the database to retrieve the requested data, including data from multiple fields of the database corresponding to multiple bean types; populating a single result set object in the bean query language with the requested data from multiple fields of the database corresponding to multiple bean types into the single result set object, wherein storing the data bypasses the steps of creating separate bean instances for the requested data and invoking individual net methods to retrieve multiple data fields, and wherein the result set object retains the relationships between the data corresponding to the original database schema; and providing access to the requested data in the result set object, wherein the result set object contains data from multiple fields of the database corresponding to multiple bean types. 13. The system of claim 9 , wherein the database language is structured query language or SQL.

1. A document processing apparatus comprising: an input device; a display device; a processor; a memory device which stores a plurality of instructions, which when executed by the processor, cause the processor to operate with the display device and the input device to: (a) detect video data designation information attached to electronic document data, the electronic document data including: (i) a first element having a first central activation value used to generate an index; (ii) a second element having a second central activation value used to generate said index; and (iii) read out audio attribute information; (b) generate a summary of said electronic document data, wherein said generation of said summary includes spreading said first central activation value to said second central activation value; (c) select video data in accordance with said detected video data designation information; (d) store a categorization model, the categorization model including a plurality of data categories; (e) create an automatic categorization based on any one of said video data and electronic document data in accordance with the categorization model; (f) update the categorization model with the automatic categorization; (g) control an output of said summary of said electronic document data such that said summary of said electronic data being output is automatically progressed based on at least one of a size of a display area and a length of time displayed; (h) control an output of said selected video data in correspondence with the output of said summary of said electronic document data such that said selected video data being output is output in synchronization with said progress of the said operation of outputting said summary of said electronic data; (i) control an output of a read out audio based on read out audio attribute information in said electronic document to synthesize said read out audio; and (j) automatically terminate the output of said video data upon completion of the outputting of said summary of said electronic document data regardless of whether an end of the video data has been reached.

1. A document processing apparatus comprising: an input device; a display device; a processor; a memory device which stores a plurality of instructions, which when executed by the processor, cause the processor to operate with the display device and the input device to: (a) detect video data designation information attached to electronic document data, the electronic document data including: (i) a first element having a first central activation value used to generate an index; (ii) a second element having a second central activation value used to generate said index; and (iii) read out audio attribute information; (b) generate a summary of said electronic document data, wherein said generation of said summary includes spreading said first central activation value to said second central activation value; (c) select video data in accordance with said detected video data designation information; (d) store a categorization model, the categorization model including a plurality of data categories; (e) create an automatic categorization based on any one of said video data and electronic document data in accordance with the categorization model; (f) update the categorization model with the automatic categorization; (g) control an output of said summary of said electronic document data such that said summary of said electronic data being output is automatically progressed based on at least one of a size of a display area and a length of time displayed; (h) control an output of said selected video data in correspondence with the output of said summary of said electronic document data such that said selected video data being output is output in synchronization with said progress of the said operation of outputting said summary of said electronic data; (i) control an output of a read out audio based on read out audio attribute information in said electronic document to synthesize said read out audio; and (j) automatically terminate the output of said video data upon completion of the outputting of said summary of said electronic document data regardless of whether an end of the video data has been reached. 7. The document processing apparatus of claim 1 , wherein a portion of said video data corresponding to a portion of said electronic document data is not output before said portion of said electronic document data is progressed to be perceivable.

8. The system of claim 7 , wherein the first user interface control is a first menu and the second user interface control is a second menu.

8. The system of claim 7 , wherein the first user interface control is a first menu and the second user interface control is a second menu. 10. The system of claim 8 , wherein the default language is the user's native language as set in profile information for the user.

9. A computer program product comprising a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a computing device, causes the computing device to: receive a design layout; generate spatial pattern clips from the design layout using a sliding window to split the design layout into overlapping windows; perform a transform on the spatial pattern clips to form frequency domain pattern clips; perform feature extraction on the frequency domain pattern clips to form frequency domain features; utilize the extracted features on a set of training samples to train a machine learning classifier model; and classify a set of previously unseen patterns, based on frequency domain features of the previously unseen patterns using the trained machine learning classifier model, into hotspots and non-hotspots.

9. A computer program product comprising a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a computing device, causes the computing device to: receive a design layout; generate spatial pattern clips from the design layout using a sliding window to split the design layout into overlapping windows; perform a transform on the spatial pattern clips to form frequency domain pattern clips; perform feature extraction on the frequency domain pattern clips to form frequency domain features; utilize the extracted features on a set of training samples to train a machine learning classifier model; and classify a set of previously unseen patterns, based on frequency domain features of the previously unseen patterns using the trained machine learning classifier model, into hotspots and non-hotspots. 18. The computer program product of claim 9 , wherein performing feature extraction comprises extracting a frequency domain feature based on a subset of a given frequency domain pattern(clip using layer, illumination, and optics information.

12. The method of claim 11 , wherein executing the query using the one or more sense descriptions in the second language comprises querying one or more documents in the second language.

12. The method of claim 11 , wherein executing the query using the one or more sense descriptions in the second language comprises querying one or more documents in the second language. 13. The method of claim 12 , further comprising translating one or more query results into the original language.

1. A computer-implemented method comprising: storing, in computer storage, event data representing user-generated events that reflect user affinities for particular items, wherein the user-generated events comprise user actions that can be monitored by a computing device; generating a score that reflects a degree to which item affinities of a first user of a plurality of users are similar to item affinities of a second user of said plurality of users, said score taking into consideration a first plurality of user-generated events by the first user reflecting a set of item affinities of the first user, and a second plurality of user-generated events by the second user reflecting a set of item affinities of the second user, wherein generating the score further comprises accessing item similarity data to determine whether an item in the set of item affinities of the first user is similar to an item in the set of item affinities of the second user; and based at least in part on the score, determining whether to output to the first user information about the second user.

1. A computer-implemented method comprising: storing, in computer storage, event data representing user-generated events that reflect user affinities for particular items, wherein the user-generated events comprise user actions that can be monitored by a computing device; generating a score that reflects a degree to which item affinities of a first user of a plurality of users are similar to item affinities of a second user of said plurality of users, said score taking into consideration a first plurality of user-generated events by the first user reflecting a set of item affinities of the first user, and a second plurality of user-generated events by the second user reflecting a set of item affinities of the second user, wherein generating the score further comprises accessing item similarity data to determine whether an item in the set of item affinities of the first user is similar to an item in the set of item affinities of the second user; and based at least in part on the score, determining whether to output to the first user information about the second user. 2. The method of claim 1 , wherein the user-generated events exclude entry of data into a profile.

1. A computer implemented method for analyzing chat leakage, comprising: providing a processor configured for obtaining chat-related information from at least one chat session between a customer and an agent; said processor configured for identifying customer leakage information from said chat to another channel; said processor configured for building a model based on said chat-related information and said leakage information; said processor configured for applying said model to provide recommendations to said agent for said customer to improve the customer's experience and accordingly prevent or reduce leakage; said applying said model further comprising: when chat leakage is identified, analyzing said chat to determine factors that have contributed to said leakage; storing data pertaining to said leakage and said analysis results in a knowledge base; and using information and analysis thereof stored in said knowledge base to train agents and to make recommendations to agents and managers to improve the customer experience.

1. A computer implemented method for analyzing chat leakage, comprising: providing a processor configured for obtaining chat-related information from at least one chat session between a customer and an agent; said processor configured for identifying customer leakage information from said chat to another channel; said processor configured for building a model based on said chat-related information and said leakage information; said processor configured for applying said model to provide recommendations to said agent for said customer to improve the customer's experience and accordingly prevent or reduce leakage; said applying said model further comprising: when chat leakage is identified, analyzing said chat to determine factors that have contributed to said leakage; storing data pertaining to said leakage and said analysis results in a knowledge base; and using information and analysis thereof stored in said knowledge base to train agents and to make recommendations to agents and managers to improve the customer experience. 5. The method of claim 1 , wherein the customer's journey comprises any of: identity of chat agents, either a voice or a text chat agent, who interacted with the customer before the customer visited a specific chat agent; a path taken by the customer to reach a chat agent; the customer's Web-log journey; and other customer information.

5. An unconstrained cursive character handwritten word recognition system, comprising a processor including: an image processing module operable to process an image of a handwritten word of one or more characters, wherein the processing of the imaged word includes segmenting the imaged word into a finite number of segments and determining a sequence of the segments using an over-segmentation-relabeling algorithm, wherein each character includes one or more consecutive segments; a feature extraction module operable to derive a feature vector to represent feature information of one segment or a combination of several consecutive segments; and a classification module operable to determine an optimal string of one or more characters as composing the imaged word, wherein the classification module uses a continuous-discrete hybrid probability modeling of features toy determine a final symbol probability of whether a given feature vector is indicative of a given distinct character, wherein, in the continuous-discrete hybrid probability modeling of N features, the features N are separated into a first group N 1 and a second group N 2 , features of the first group N 1 are distributed using a continuous probability model to obtain a continuous distribution probability measure, features of the second group N 2 , are distributed using a discrete probability model given by equation (1) b j ⁡ ( O ) = ∏ i = 1 N 2 ⁢ ⁢ P ⁡ ( s i ) ( 1 ) wherein, in Equation (1), b j (O) is the discrete symbol probability distribution of the features of the second group N 2 for an observation O composed of the one segment or the combination of several consecutive segments, wherein P(s i ) is the probability of s i , and s i is the i-th feature of the observation, and wherein the continuous distribution probability measure and the discrete distribution probability measure b j (O) are multiplied and normalized to obtain the final symbol probability of whether a given feature vector is indicative of a given distinct character.

5. An unconstrained cursive character handwritten word recognition system, comprising a processor including: an image processing module operable to process an image of a handwritten word of one or more characters, wherein the processing of the imaged word includes segmenting the imaged word into a finite number of segments and determining a sequence of the segments using an over-segmentation-relabeling algorithm, wherein each character includes one or more consecutive segments; a feature extraction module operable to derive a feature vector to represent feature information of one segment or a combination of several consecutive segments; and a classification module operable to determine an optimal string of one or more characters as composing the imaged word, wherein the classification module uses a continuous-discrete hybrid probability modeling of features toy determine a final symbol probability of whether a given feature vector is indicative of a given distinct character, wherein, in the continuous-discrete hybrid probability modeling of N features, the features N are separated into a first group N 1 and a second group N 2 , features of the first group N 1 are distributed using a continuous probability model to obtain a continuous distribution probability measure, features of the second group N 2 , are distributed using a discrete probability model given by equation (1) b j ⁡ ( O ) = ∏ i = 1 N 2 ⁢ ⁢ P ⁡ ( s i ) ( 1 ) wherein, in Equation (1), b j (O) is the discrete symbol probability distribution of the features of the second group N 2 for an observation O composed of the one segment or the combination of several consecutive segments, wherein P(s i ) is the probability of s i , and s i is the i-th feature of the observation, and wherein the continuous distribution probability measure and the discrete distribution probability measure b j (O) are multiplied and normalized to obtain the final symbol probability of whether a given feature vector is indicative of a given distinct character. 12. The handwritten word recognition system of claim 5 , wherein the imaged word is an Arabic word.

25. A computer-program product, tangibly embodied in a machine-readable non-transitory storage medium, including instructions executable to cause a data processing apparatus to perform operations including: storing data in a computerized data storage system that facilitates collaborative data management, wherein collaborative data management includes performance of multiple data management tasks, each data management task associated with a different one of multiple classes of data management tasks, wherein each one of the classes of data management tasks is associated with a unique group of users having permission to perform the data management tasks of the one class; activating a definition interface for defining terms used to manage the data, wherein a term is applicable to the data, and wherein a term includes a definition or a requirement; activating an instruction interface for effectuating terms, wherein the instruction interface facilitates an input of instructions into a data management system such that the inputted instructions effectuate a defined term within the data storage system, and wherein the inputted instructions cause the data storage system to associate the data with the defined term effectuated by the inputted instructions; processing the data according to the defined term effectuated by the inputted instructions; and displaying the inputted instructions, the defined term effectuated by the inputted instructions, and the processed data, wherein displaying includes using a monitoring interface that facilitates monitoring the data stored in the data storage system.

25. A computer-program product, tangibly embodied in a machine-readable non-transitory storage medium, including instructions executable to cause a data processing apparatus to perform operations including: storing data in a computerized data storage system that facilitates collaborative data management, wherein collaborative data management includes performance of multiple data management tasks, each data management task associated with a different one of multiple classes of data management tasks, wherein each one of the classes of data management tasks is associated with a unique group of users having permission to perform the data management tasks of the one class; activating a definition interface for defining terms used to manage the data, wherein a term is applicable to the data, and wherein a term includes a definition or a requirement; activating an instruction interface for effectuating terms, wherein the instruction interface facilitates an input of instructions into a data management system such that the inputted instructions effectuate a defined term within the data storage system, and wherein the inputted instructions cause the data storage system to associate the data with the defined term effectuated by the inputted instructions; processing the data according to the defined term effectuated by the inputted instructions; and displaying the inputted instructions, the defined term effectuated by the inputted instructions, and the processed data, wherein displaying includes using a monitoring interface that facilitates monitoring the data stored in the data storage system. 26. The computer-program product of claim 25 , wherein the instruction interface facilitates the input of instructions through code or instructions.

10. A method on a computer for enabling a user to view and interact with a visual map in an external application, the method comprising: creating a file, using a visual mapping application, that can be executed by an external application, the visual mapping application having visual mapping functionality including editing functionality for a user to edit visual map data and store the edited visual map data; and inserting data related to a visual map, including any stored edits to the visual map data, into the created file; wherein the created file, when executed by an external application, is able to display the visual map and provide a user with at least a subset of the same visual mapping functionality as the visual mapping application within the external application, wherein the visual mapping functionality includes the editing functionality for editing the visual map.

10. A method on a computer for enabling a user to view and interact with a visual map in an external application, the method comprising: creating a file, using a visual mapping application, that can be executed by an external application, the visual mapping application having visual mapping functionality including editing functionality for a user to edit visual map data and store the edited visual map data; and inserting data related to a visual map, including any stored edits to the visual map data, into the created file; wherein the created file, when executed by an external application, is able to display the visual map and provide a user with at least a subset of the same visual mapping functionality as the visual mapping application within the external application, wherein the visual mapping functionality includes the editing functionality for editing the visual map. 20. The method of claim 10 , wherein the visual mapping functionality includes the ability to expand and collapse topics in the visual map, change a zoom factor of the visual map, follow hypertext links in the visual map, and print the visual map.

8. The system of claim 7 , further comprising generating the audible response based on the set of dialog actions and via a machine learning algorithm.

8. The system of claim 7 , further comprising generating the audible response based on the set of dialog actions and via a machine learning algorithm. 9. The system of claim 8 , further comprising augmenting the machine learning algorithm using reinforcement learning.

13. The processor-implemented method of claim 12 , wherein the identified attachments are ranked based on at least one of font size, capitalization, boldening effect, color scheme, italicization, or underlining effect of the at least one search term with respect to the average format of the text in the attachment.

13. The processor-implemented method of claim 12 , wherein the identified attachments are ranked based on at least one of font size, capitalization, boldening effect, color scheme, italicization, or underlining effect of the at least one search term with respect to the average format of the text in the attachment. 14. The processor-implemented method of claim 13 , wherein presenting the results comprises presenting the content of attachments according to the rank.

16. A system comprising: a data store for storing content items; and one or more processors configured to interact with the data store, the one or more processors being further configured to perform operations comprising: identifying an entity that is referenced by a received search query; identifying a plurality of content items that have been identified as relevant to the entity; for each of the identified content items, identifying: a content category to which the content item has been classified; and a rank score indicative of the relevance of the content item to the entity; selecting a set of knowledge modules to be presented in a knowledge panel for the entity, the knowledge panel (i) being a portion of a search results page in which content related to the entity is presented separate from a listing of search results for the received search query and (ii) including presentation space in which knowledge modules are presented in multiple different arrangements, the set of knowledge modules including at least two different knowledge module types that each presents a different category of content items in a given data format, the selection being based, at least in part, on the rank scores and identified content categories for the content items, wherein the set of knowledge modules include: a first knowledge module, of a first type, that presents a first category of content items in a first data format; and a second knowledge module, of a second type different from the first type, that presents a second category of content items different from the first category of content items, the second knowledge module including a first content item obtained from a first network resource and a second content item obtained from a second network resource different from the first network resource, the second category of content items having a second data format different from the first data format; determining, based on a combination of the rank scores for two or more content items assigned to each knowledge module, an order in which the set of knowledge modules are to be presented in the knowledge panel; positioning the knowledge module within the knowledge panel in the determined order for the knowledge modules; determining, for a given knowledge module of the set, an order in which the two or more content items assigned to the given knowledge module are to be presented in the given knowledge module based on the rank score for the two or more content items assigned to the given knowledge module; and providing, to a user device, the knowledge panel including the set of knowledge modules that are presented in the determined order for the set of knowledge modules, the knowledge panel being presented with the search results page for the received search query, and the two or more content items assigned to the given knowledge module being presented in the determined order for the two or more content items assigned to the given knowledge module.

16. A system comprising: a data store for storing content items; and one or more processors configured to interact with the data store, the one or more processors being further configured to perform operations comprising: identifying an entity that is referenced by a received search query; identifying a plurality of content items that have been identified as relevant to the entity; for each of the identified content items, identifying: a content category to which the content item has been classified; and a rank score indicative of the relevance of the content item to the entity; selecting a set of knowledge modules to be presented in a knowledge panel for the entity, the knowledge panel (i) being a portion of a search results page in which content related to the entity is presented separate from a listing of search results for the received search query and (ii) including presentation space in which knowledge modules are presented in multiple different arrangements, the set of knowledge modules including at least two different knowledge module types that each presents a different category of content items in a given data format, the selection being based, at least in part, on the rank scores and identified content categories for the content items, wherein the set of knowledge modules include: a first knowledge module, of a first type, that presents a first category of content items in a first data format; and a second knowledge module, of a second type different from the first type, that presents a second category of content items different from the first category of content items, the second knowledge module including a first content item obtained from a first network resource and a second content item obtained from a second network resource different from the first network resource, the second category of content items having a second data format different from the first data format; determining, based on a combination of the rank scores for two or more content items assigned to each knowledge module, an order in which the set of knowledge modules are to be presented in the knowledge panel; positioning the knowledge module within the knowledge panel in the determined order for the knowledge modules; determining, for a given knowledge module of the set, an order in which the two or more content items assigned to the given knowledge module are to be presented in the given knowledge module based on the rank score for the two or more content items assigned to the given knowledge module; and providing, to a user device, the knowledge panel including the set of knowledge modules that are presented in the determined order for the set of knowledge modules, the knowledge panel being presented with the search results page for the received search query, and the two or more content items assigned to the given knowledge module being presented in the determined order for the two or more content items assigned to the given knowledge module. 17. The system of claim 16 , wherein selecting the set of knowledge modules comprises: determining, for two or more content categories, a preferred module type indicative of a type of knowledge module in which presentation of the content items of the two or more content categories is preferred; determining that a particular module type is the preferred module type for two of the content categories; and determining that content items for only a first of the two content categories will be presented in the particular module type, the determination being based, in part, on panel constraints that specify a maximum number of the knowledge modules of the particular module type that are allowed to be included in the knowledge panel.

18. A method for searching an applied data model, comprising: translating a query to a set of statements operable to search the applied data model to an arbitrary level, wherein the applied data model comprises a plurality of defined data structures, wherein each of the data structures comprises one or more fields or properties associated with the data structure, wherein all data structures of the same type contain the same properties, wherein the applied data model comprises at least one component and a relationship corresponding to the at least one component, wherein the at least one component represents a physical or logical entity in the arbitrarily complex environment, wherein each component has a set of fields which contains information relating to an atomic entity associated with the at least one component, wherein the set of fields comprises: a set of property fields containing information about the attributes or characteristics of the component; and a field that contains a link to its component type, wherein values assigned to the properties in the component are based on the attributes of the entity which the component was instantiated to represent, wherein the relationship represents an association between the physical or logical entity and other physical or logical entities in the arbitrarily complex environment, wherein the relationship comprises: a field that is a foreign key to its relationship type; and a set of property fields containing information about one or more of the attributes of the relationship, wherein the components are stored in a schema, wherein property definitions of each component are linked to a type of component, wherein changes made to the type of component are automatically associated with all components of that type of component without changing the schema to reflect a corresponding change in the arbitrarily complex environment, wherein the schema is implemented in a database, and wherein the query is a component query or a relationship query in a first query language, wherein the set of statements is capable of execution by a database management system supporting a second query language, wherein the database management system is associated with the database, and wherein the set of statements is tailored to a table schema which implements the applied data model; searching the applied data model to the arbitrary level based on the set of statements, wherein the set of statements implements a graph search; producing a set of results to the set of statements, wherein the set of results includes at least one component or one relationship at the arbitrary level; and processing the set of results according to the query, wherein processing the set of results includes structuring the set of results based on the query.

18. A method for searching an applied data model, comprising: translating a query to a set of statements operable to search the applied data model to an arbitrary level, wherein the applied data model comprises a plurality of defined data structures, wherein each of the data structures comprises one or more fields or properties associated with the data structure, wherein all data structures of the same type contain the same properties, wherein the applied data model comprises at least one component and a relationship corresponding to the at least one component, wherein the at least one component represents a physical or logical entity in the arbitrarily complex environment, wherein each component has a set of fields which contains information relating to an atomic entity associated with the at least one component, wherein the set of fields comprises: a set of property fields containing information about the attributes or characteristics of the component; and a field that contains a link to its component type, wherein values assigned to the properties in the component are based on the attributes of the entity which the component was instantiated to represent, wherein the relationship represents an association between the physical or logical entity and other physical or logical entities in the arbitrarily complex environment, wherein the relationship comprises: a field that is a foreign key to its relationship type; and a set of property fields containing information about one or more of the attributes of the relationship, wherein the components are stored in a schema, wherein property definitions of each component are linked to a type of component, wherein changes made to the type of component are automatically associated with all components of that type of component without changing the schema to reflect a corresponding change in the arbitrarily complex environment, wherein the schema is implemented in a database, and wherein the query is a component query or a relationship query in a first query language, wherein the set of statements is capable of execution by a database management system supporting a second query language, wherein the database management system is associated with the database, and wherein the set of statements is tailored to a table schema which implements the applied data model; searching the applied data model to the arbitrary level based on the set of statements, wherein the set of statements implements a graph search; producing a set of results to the set of statements, wherein the set of results includes at least one component or one relationship at the arbitrary level; and processing the set of results according to the query, wherein processing the set of results includes structuring the set of results based on the query. 19. The method of claim 18 , wherein the query specifies the arbitrary level.

11. A tangible computer readable storage medium, comprising computer readable instructions which, when executed, cause a processor to at least: access interaction data and contact data for a user of a first social networking site via an interface provided by the first social networking site; determine a connectedness for the user based on the contact data; determine an interactivity for the user based on the interaction data; determine a network constancy for the user by determining a ratio of (a) connections between contacts of the user of the first social networking site to (b) at least one of: (1) broken connections between the contacts of the user in response to removal of the user from the first social networking site; or (2) connections between the contacts of the user that exhibit changed degrees of connection in response to removal of the user from the first social networking site; determine a first network efficacy of the first social networking site based on the connectedness, the interactivity, and the network constancy, the network constancy being based on the ratio; and transmit the first network efficacy to an advertising server to facilitate delivery of an advertisement to the user of the first social networking site.

11. A tangible computer readable storage medium, comprising computer readable instructions which, when executed, cause a processor to at least: access interaction data and contact data for a user of a first social networking site via an interface provided by the first social networking site; determine a connectedness for the user based on the contact data; determine an interactivity for the user based on the interaction data; determine a network constancy for the user by determining a ratio of (a) connections between contacts of the user of the first social networking site to (b) at least one of: (1) broken connections between the contacts of the user in response to removal of the user from the first social networking site; or (2) connections between the contacts of the user that exhibit changed degrees of connection in response to removal of the user from the first social networking site; determine a first network efficacy of the first social networking site based on the connectedness, the interactivity, and the network constancy, the network constancy being based on the ratio; and transmit the first network efficacy to an advertising server to facilitate delivery of an advertisement to the user of the first social networking site. 13. The storage medium as defined in claim 11 , wherein the instructions are to cause the processor to determine the connectedness based on a number of first-degree contacts of the user and a number of second-degree contacts of the user, and the instructions are to cause the processor to determine the connectedness by assigning a first weight to a first one of the first-degree contacts who has restricted a connection with the user and assigning a second weight to a second one of the first-degree contacts who has not restricted a connection with the user.

1. A method of performing an action associated with a gesture performed by a user of a device having a processor and a sensor providing a sensor output, the method comprising: executing on the processor instructions that cause the device to: transition the device to a training mode upon receiving, from the user, a training start, request; during the training mode: while the user performs the gesture, monitor the sensor to detect an identified sensor output identifying the gesture during a training period between the training start request and the training completion request; identify, during the training period, a restricted period that is shorter than the training period and during which the identified sensor output identifies the gesture; associate with the gesture the identified sensor output of the sensor during the restricted period; and associate an action with the gesture; transition the device to a recognition mode upon receiving, from the user, a training completion request; and during the recognition mode: monitor the sensor output of the sensor; and upon detecting that the sensor output matches the identified sensor output associated with the gesture, perform the action associated with the gesture.

1. A method of performing an action associated with a gesture performed by a user of a device having a processor and a sensor providing a sensor output, the method comprising: executing on the processor instructions that cause the device to: transition the device to a training mode upon receiving, from the user, a training start, request; during the training mode: while the user performs the gesture, monitor the sensor to detect an identified sensor output identifying the gesture during a training period between the training start request and the training completion request; identify, during the training period, a restricted period that is shorter than the training period and during which the identified sensor output identifies the gesture; associate with the gesture the identified sensor output of the sensor during the restricted period; and associate an action with the gesture; transition the device to a recognition mode upon receiving, from the user, a training completion request; and during the recognition mode: monitor the sensor output of the sensor; and upon detecting that the sensor output matches the identified sensor output associated with the gesture, perform the action associated with the gesture. 7. The method of claim 1 , wherein monitoring the sensor to detect the identified sensor output further comprises: during respective at least two gesture performances of the gesture by the user, monitoring the sensor to detect the identified sensor output identifying the gesture during the gesture performance; and determining the identified sensor output according to the identified sensor outputs of the respective at least two gesture performances.

1. A computer-readable storage medium having computer-executable instructions stored thereon which, when executed by a computer system, cause the computer system to perform a method comprising: instantiating at least one iterator in response to a search request, wherein the iterator includes fixed state information that remains constant over a life of the iterator, and includes dynamic state information that is updated over the life of the iterator; creating a storage structure associated with the iterator and storing a representation of the dynamic state information in the storage structure; creating a stack structure that includes a plurality of entries, further storing the representation of the dynamic state information in one of the entries, and further storing at least a further representation of a further instance of the dynamic state information in a further one of the entries; traversing the iterator through at least a portion of at least one postings list in connection with performing a search in response to the search request; updating at least one instance of the dynamic state information, as the iterator traverses through at least the portion of the postings list while performing the search; and evaluating whether to create a checkpoint of the iterator at one or more points during the iterator traversing through at least the portion of the postings list while performing the search, wherein the checkpoint includes at least a representation of the dynamic state information at one of the points.

1. A computer-readable storage medium having computer-executable instructions stored thereon which, when executed by a computer system, cause the computer system to perform a method comprising: instantiating at least one iterator in response to a search request, wherein the iterator includes fixed state information that remains constant over a life of the iterator, and includes dynamic state information that is updated over the life of the iterator; creating a storage structure associated with the iterator and storing a representation of the dynamic state information in the storage structure; creating a stack structure that includes a plurality of entries, further storing the representation of the dynamic state information in one of the entries, and further storing at least a further representation of a further instance of the dynamic state information in a further one of the entries; traversing the iterator through at least a portion of at least one postings list in connection with performing a search in response to the search request; updating at least one instance of the dynamic state information, as the iterator traverses through at least the portion of the postings list while performing the search; and evaluating whether to create a checkpoint of the iterator at one or more points during the iterator traversing through at least the portion of the postings list while performing the search, wherein the checkpoint includes at least a representation of the dynamic state information at one of the points. 6. The storage medium of claim 1 , wherein the instructions for evaluating whether to create a checkpoint include instructions for directing the iterator to create a checkpoint, in response to a checkpoint command received from a parent iterator.

11. The logic of claim 10 , wherein calculating the operational dependence role comprises: identifying a protocol of a data packet at the at least one port; determining a protocol importance score of the protocol, wherein the protocol importance score comprises a numerical score assigned to the protocol based on a relative importance of the protocol in the network environment; calculating a connection average for the asset; determining a connection score of the connection average, and calculating a service dependence score for the at least one service as a function of the connection score and the protocol importance score.

11. The logic of claim 10 , wherein calculating the operational dependence role comprises: identifying a protocol of a data packet at the at least one port; determining a protocol importance score of the protocol, wherein the protocol importance score comprises a numerical score assigned to the protocol based on a relative importance of the protocol in the network environment; calculating a connection average for the asset; determining a connection score of the connection average, and calculating a service dependence score for the at least one service as a function of the connection score and the protocol importance score. 13. The logic of claim 11 , the processor being operable to perform further instructions comprising: calculating a respective service dependence score for each service running on the asset; sorting the respective service dependence scores; and calculating the host dependence score as an interactive function of the respective service dependence scores.

1. A computer implemented method for recognition of a handwritten pattern comprising one or more curves, said method comprising: receiving, by a computational device, sample data representing the handwritten pattern; segmenting, by the computational device, the handwritten pattern by detecting segmentation points on each curve and by dividing the handwritten pattern into segments, wherein detecting segmentation points further comprises detecting the segmentation points as local extreme points which are below a predetermined threshold, such that a particular gradient function of the curve is exceeded; and comparing, by the computational device, the handwritten pattern to templates wherein the comparing comprises: normalizing, by the computational device, said segments according to a scheme which is independent of the templates to which the segments are to be compared, and determining, by the computational device, matching measures for selecting at least one sequence of templates representing a recognition candidate of the handwritten pattern.

1. A computer implemented method for recognition of a handwritten pattern comprising one or more curves, said method comprising: receiving, by a computational device, sample data representing the handwritten pattern; segmenting, by the computational device, the handwritten pattern by detecting segmentation points on each curve and by dividing the handwritten pattern into segments, wherein detecting segmentation points further comprises detecting the segmentation points as local extreme points which are below a predetermined threshold, such that a particular gradient function of the curve is exceeded; and comparing, by the computational device, the handwritten pattern to templates wherein the comparing comprises: normalizing, by the computational device, said segments according to a scheme which is independent of the templates to which the segments are to be compared, and determining, by the computational device, matching measures for selecting at least one sequence of templates representing a recognition candidate of the handwritten pattern. 2. The computer implemented method according to claim 1 , wherein said matching measures comprise segmental matching measures comparing segmental features of the handwritten pattern to segmental features of the templates.

28. The method of claim 27 , further comprising providing, through the online marketplace, the translated portion of the work for one or more readers to consume.

28. The method of claim 27 , further comprising providing, through the online marketplace, the translated portion of the work for one or more readers to consume. 29. The method of claim 28 , further comprising determining, based at least in part on a number of readers that consume the translated portion, a value of the work.

7. The document processing apparatus according to claim 1 , further comprising: a filter storing unit configured to store a plurality of filters associated with application programs, respectively; and a filter selecting unit configured to select a filter associated with the first application program from the filter storing unit, wherein the character string extracting unit is configured to detect the one or more character strings using the filter selected by the filter selecting unit.

7. The document processing apparatus according to claim 1 , further comprising: a filter storing unit configured to store a plurality of filters associated with application programs, respectively; and a filter selecting unit configured to select a filter associated with the first application program from the filter storing unit, wherein the character string extracting unit is configured to detect the one or more character strings using the filter selected by the filter selecting unit. 9. The document processing apparatus according to claim 7 , further comprising: a user setting unit configure to display one or more filters stored by the filter storing unit as a candidate for selection to the user to enable the user to select at least one of the one or more filters to be used in the character string extracting unit in preference to a relationship between an application program and a filter determined by the filter storing unit.

1. A computer-implemented method of ranking geographic information, the method comprising: identifying at least two documents retrieved in response to a query, a first document of the at least two documents being associated with a first geographic feature, and a second document of the at least two documents being associated with a second geographic feature; identifying types of the first and the second geographic features; identifying a first ranking function based at least in part on the identified type of the first geographic feature and a second ranking function based at least in part on the identified type of the second geographic feature; generating a first rank score for the first geographic feature based at least in part on the first ranking function and on one or more physical properties of the first geographic feature, and generating a second rank score for the second geographic feature based at least in part on the second ranking function and on one or more physical properties of the second geographic feature, wherein generating the first and the second rank scores is performed, at least in part, independently of a structure of the first and second documents and includes measuring a degree of usefulness of each geographic feature of the at least two documents to at least one user, and wherein each of the one or more physical properties of the first and the second geographic features provides an indication of the usefulness of the first and the second geographic features; and providing a list of the at least two documents that is ordered based at least in part on the first and the second rank scores of the first and the second geographic features.

1. A computer-implemented method of ranking geographic information, the method comprising: identifying at least two documents retrieved in response to a query, a first document of the at least two documents being associated with a first geographic feature, and a second document of the at least two documents being associated with a second geographic feature; identifying types of the first and the second geographic features; identifying a first ranking function based at least in part on the identified type of the first geographic feature and a second ranking function based at least in part on the identified type of the second geographic feature; generating a first rank score for the first geographic feature based at least in part on the first ranking function and on one or more physical properties of the first geographic feature, and generating a second rank score for the second geographic feature based at least in part on the second ranking function and on one or more physical properties of the second geographic feature, wherein generating the first and the second rank scores is performed, at least in part, independently of a structure of the first and second documents and includes measuring a degree of usefulness of each geographic feature of the at least two documents to at least one user, and wherein each of the one or more physical properties of the first and the second geographic features provides an indication of the usefulness of the first and the second geographic features; and providing a list of the at least two documents that is ordered based at least in part on the first and the second rank scores of the first and the second geographic features. 12. The method of claim 1 further comprising receiving information about geographic features, wherein receiving the information includes receiving a document that describes the geographic feature, and generating the first and the second rank scores comprises generating the first and the second rank scores independent of a structure of the document describing the geographic feature.

9. A method of operating an electronic device comprising the steps of: detecting a content entry; receiving a request by a user for a content prediction; identifying a most probable next content prediction by using a personalized and learning database, wherein the personalized and learning database comprises recently used data, said data comprising any of: one or more word associations, one or more context associations, one or more sensitivity associations, one or more Uniform Resource Locators, and one or more electronic mail addresses, and by using content stored in user interface memory, wherein the prediction is a customized depth of prediction, where depth of prediction enables the user to indicate whether a character, word, or phrase is predicted, said identifying comprising: storing the personalized and learning database in memory; storing content and sensitivity associations of entered content in an associations memory; storing one or more language dictionaries as chosen by a user in a main dictionaries memory; receiving, by the personalized and learning database, inputs from the associations memory, the main dictionaries memory, and a recent entries memory; using the inputs, re-sorting said recently used data in the personalized and learning database combined with content used by the user and consequently customizing re-ordering of content based on personalized usage; and using the re-ordered content stored in the personalized and learning database and using content stored in user interface memory to provide a customized depth of prediction in predicting the most probable completion alternative by allowing the user to control the depth of prediction by providing user input that indicates whether the entire prediction or one or more portions of the prediction are accepted and wherein depth of prediction indicates whether a predicted character, predicted word, or predicted words encompassed within a phrase all at once is accepted; displaying the most probable next content prediction; determining whether a user has accepted the most probable next content prediction; and adding the most probable next content prediction to the content entry when the user has accepted the most probable next content prediction; wherein a user interface is provided that comprises a navigation key having a first set of controls and a second set of controls; wherein said first set of controls are configured for acceptance or non-acceptance of the most probable next content prediction currently displayed at the display when in an editing mode and said second set of controls are configured for changing or overriding the most probable next content prediction currently displayed at the display when in the editing mode; wherein said first of controls are configured for scrolling a cursor right one character at a time or scrolling the cursor left one character at a time when said first set of controls are in a navigation mode; wherein said second set of controls are configured for scrolling the cursor down one line at a time or scrolling the cursor up one line at a time, when said second set of controls are in the navigation mode; and wherein: a right control of said first set of controls is configured for, in navigation mode and in a hold and press mode, jumping the cursor to the right one word at a time; a left control of said first set of controls is configured for, in navigation mode and in the hold and press mode, jumping the cursor left one word at a time; the left control being further configured for, in editing mode and in the hold and press mode, dismissing prediction and locking a last key press entry.

9. A method of operating an electronic device comprising the steps of: detecting a content entry; receiving a request by a user for a content prediction; identifying a most probable next content prediction by using a personalized and learning database, wherein the personalized and learning database comprises recently used data, said data comprising any of: one or more word associations, one or more context associations, one or more sensitivity associations, one or more Uniform Resource Locators, and one or more electronic mail addresses, and by using content stored in user interface memory, wherein the prediction is a customized depth of prediction, where depth of prediction enables the user to indicate whether a character, word, or phrase is predicted, said identifying comprising: storing the personalized and learning database in memory; storing content and sensitivity associations of entered content in an associations memory; storing one or more language dictionaries as chosen by a user in a main dictionaries memory; receiving, by the personalized and learning database, inputs from the associations memory, the main dictionaries memory, and a recent entries memory; using the inputs, re-sorting said recently used data in the personalized and learning database combined with content used by the user and consequently customizing re-ordering of content based on personalized usage; and using the re-ordered content stored in the personalized and learning database and using content stored in user interface memory to provide a customized depth of prediction in predicting the most probable completion alternative by allowing the user to control the depth of prediction by providing user input that indicates whether the entire prediction or one or more portions of the prediction are accepted and wherein depth of prediction indicates whether a predicted character, predicted word, or predicted words encompassed within a phrase all at once is accepted; displaying the most probable next content prediction; determining whether a user has accepted the most probable next content prediction; and adding the most probable next content prediction to the content entry when the user has accepted the most probable next content prediction; wherein a user interface is provided that comprises a navigation key having a first set of controls and a second set of controls; wherein said first set of controls are configured for acceptance or non-acceptance of the most probable next content prediction currently displayed at the display when in an editing mode and said second set of controls are configured for changing or overriding the most probable next content prediction currently displayed at the display when in the editing mode; wherein said first of controls are configured for scrolling a cursor right one character at a time or scrolling the cursor left one character at a time when said first set of controls are in a navigation mode; wherein said second set of controls are configured for scrolling the cursor down one line at a time or scrolling the cursor up one line at a time, when said second set of controls are in the navigation mode; and wherein: a right control of said first set of controls is configured for, in navigation mode and in a hold and press mode, jumping the cursor to the right one word at a time; a left control of said first set of controls is configured for, in navigation mode and in the hold and press mode, jumping the cursor left one word at a time; the left control being further configured for, in editing mode and in the hold and press mode, dismissing prediction and locking a last key press entry. 18. The method of operating an electronic device as defined in claim 9 , further comprising the steps of: receiving further content entry from a user input.

9. A computer program product, comprising a non-transitory computer-readable medium having a computer-readable program code embodied therein, the computer-readable program code including instructions adapted to be executed by one or more processors to: receive a request for a new component for an enterprise application, the enterprise application being accessed through a web browser associated with a client computing system, the new component being associated with a first scripting language resource independent of the client computing system, wherein a scripting language resource comprises scripting language source code that provides one or more functionalities for the enterprise application; identify, in response to the request from the client computing system, all scripting language resources that the requested first scripting language resource depends on based on dependency information, wherein the dependency information identifies dependencies between the first scripting language resource and the scripting language resources that the requested first scripting language resource depends on, the scripting language resources comprising two or more scripting language resources stored on a server computing system that includes the one or more processors; determine a dependency order of the requested first scripting language resource and all the identified scripting language resources based on the dependency information; and transmit, to a web browser associated with the client computing system, a single response to the received request, the single response including the requested first scripting language resource and all the identified scripting language resources that the requested first scripting language resource depends on in the determined dependency order.

9. A computer program product, comprising a non-transitory computer-readable medium having a computer-readable program code embodied therein, the computer-readable program code including instructions adapted to be executed by one or more processors to: receive a request for a new component for an enterprise application, the enterprise application being accessed through a web browser associated with a client computing system, the new component being associated with a first scripting language resource independent of the client computing system, wherein a scripting language resource comprises scripting language source code that provides one or more functionalities for the enterprise application; identify, in response to the request from the client computing system, all scripting language resources that the requested first scripting language resource depends on based on dependency information, wherein the dependency information identifies dependencies between the first scripting language resource and the scripting language resources that the requested first scripting language resource depends on, the scripting language resources comprising two or more scripting language resources stored on a server computing system that includes the one or more processors; determine a dependency order of the requested first scripting language resource and all the identified scripting language resources based on the dependency information; and transmit, to a web browser associated with the client computing system, a single response to the received request, the single response including the requested first scripting language resource and all the identified scripting language resources that the requested first scripting language resource depends on in the determined dependency order. 12. The non-transitory computer-readable medium of claim 9 , wherein the request includes dependency information that declares dependencies between the first scripting language resource and the scripting language resources that the requested first scripting language resource depends on.

1. A method for analyzing ambiguities in language for natural language processing, said method comprising: an input device receiving a first sentence or phrase from a source; wherein a vocabulary database stores words or phrases; wherein a language grammar template database stores language grammar templates; an analyzer module segmenting said first sentence or phrase, using words or phrases obtained from said vocabulary database and language grammar templates obtained from said language grammar template database; said analyzer module parsing said first sentence or phrase into one or more sentence or phrase components; said analyzer module determining Z-valuation for said one or more sentence or phrase components as a value of an attribute for said one or more sentence or phrase components; wherein said Z-valuation for said one or more sentence or phrase components are based on one or more parameters with unsharp class boundary or fuzzy membership function; said analyzer module processing language ambiguities in said first sentence or phrase for natural language processing, using said Z-valuation for said one or more sentence or phrase components.

1. A method for analyzing ambiguities in language for natural language processing, said method comprising: an input device receiving a first sentence or phrase from a source; wherein a vocabulary database stores words or phrases; wherein a language grammar template database stores language grammar templates; an analyzer module segmenting said first sentence or phrase, using words or phrases obtained from said vocabulary database and language grammar templates obtained from said language grammar template database; said analyzer module parsing said first sentence or phrase into one or more sentence or phrase components; said analyzer module determining Z-valuation for said one or more sentence or phrase components as a value of an attribute for said one or more sentence or phrase components; wherein said Z-valuation for said one or more sentence or phrase components are based on one or more parameters with unsharp class boundary or fuzzy membership function; said analyzer module processing language ambiguities in said first sentence or phrase for natural language processing, using said Z-valuation for said one or more sentence or phrase components. 6. The method for analyzing ambiguities in language for natural language processing as recited in claim 1 , wherein said method comprises: receiving said first sentence or phrase from a search engine module.

1. A system for creating hyperlinks within electronic text files that link to multimedia video content, wherein the multimedia video content is contextually related to the electronic text files, comprising: a content database storing electronic text files; a video database storing multimedia video content; a web server configured to send content to requesting user devices, the content including the electronic text files and the multimedia video content; a hyperlink generation module in communication with the content database and the video database, configured to execute computer-implemented steps of: retrieving an electronic text file from the content database; analyzing the electronic text file by term weighting words in the electronic text file to identify one or more keywords contextually relevant to the electronic text file; for at least one keyword of the one or more keywords: querying a video database to identify at least one multimedia video content item related to the at least one keyword, the multimedia video content item identified by comparing the at least one keyword to metadata associated with the multimedia video content; generating at least one hyperlink for the keyword, the generated hyperlink including a pointer to the identified multimedia video content item related to the at least one keyword; inserting a keyword hyperlink into the electronic text file prior to the web server sending the electronic text file to a user device, the keyword hyperlink linking the at least one keyword to a display based on the generated hyperlink; responsive to inserting the keyword hyperlink into the electronic text file, and prior to receiving a request for the multimedia video content item, copying the multimedia video content item from the video database into a particular memory, from which the web server is configured to quickly access the multimedia video content when requested by the user device from the electronic text file.

1. A system for creating hyperlinks within electronic text files that link to multimedia video content, wherein the multimedia video content is contextually related to the electronic text files, comprising: a content database storing electronic text files; a video database storing multimedia video content; a web server configured to send content to requesting user devices, the content including the electronic text files and the multimedia video content; a hyperlink generation module in communication with the content database and the video database, configured to execute computer-implemented steps of: retrieving an electronic text file from the content database; analyzing the electronic text file by term weighting words in the electronic text file to identify one or more keywords contextually relevant to the electronic text file; for at least one keyword of the one or more keywords: querying a video database to identify at least one multimedia video content item related to the at least one keyword, the multimedia video content item identified by comparing the at least one keyword to metadata associated with the multimedia video content; generating at least one hyperlink for the keyword, the generated hyperlink including a pointer to the identified multimedia video content item related to the at least one keyword; inserting a keyword hyperlink into the electronic text file prior to the web server sending the electronic text file to a user device, the keyword hyperlink linking the at least one keyword to a display based on the generated hyperlink; responsive to inserting the keyword hyperlink into the electronic text file, and prior to receiving a request for the multimedia video content item, copying the multimedia video content item from the video database into a particular memory, from which the web server is configured to quickly access the multimedia video content when requested by the user device from the electronic text file. 3. The system of claim 1 , wherein the electronic text files include one or more of: electronic documents, news and other content-related articles, blog postings, message board postings, threaded discussions, email messages, text-based computer-readable files, HyperText Markup Language (HTML) files, Extensible HyperText Markup Language (XHTML) files, Extensible Markup Language (XML) files, or webpages.

1. A method of linking elements in a computer-generated document to corresponding data in a database, comprising: attaching a schema file associated with at least one intended use of the document to a document defining rules associated with a markup language to be applied to the document, wherein the markup language is XML and wherein the rules associated with the markup language to be applied to the document comprise names of elements of the markup language and data types associated with the names of the elements of the markup language; applying the elements of the markup language to the document; establishing data fields within the database for linking to corresponding markup language elements in the document; writing a unique document identifier number to the document for linking the data fields in the database to the document, wherein linking the data fields in the database to the document comprises: determining if a table associated with the document exists within a document library; if no table is associated with the document, creating a table containing user-defined elements associated with the document; selecting a table within a document library, the document library being maintained in the database where the table is associated with the document, and linking at least one markup language element in the document to corresponding data in the database; when data is entered into the database associated with the given markup language element in the document, automatically writing the data to the document in a location in the document associated with the given markup language element; when the given markup language element in the document is modified, automatically updating the corresponding data in the database; providing at least one suggested document element according to the schema file associated with the at least one intended use of the document, wherein the at least one suggested document element comprises an element structure linked to at least one corresponding data field in the database; and enforcing at least one element constraint according to the schema file, wherein the element constraint comprises at least one piece of required data for at least one document element.

1. A method of linking elements in a computer-generated document to corresponding data in a database, comprising: attaching a schema file associated with at least one intended use of the document to a document defining rules associated with a markup language to be applied to the document, wherein the markup language is XML and wherein the rules associated with the markup language to be applied to the document comprise names of elements of the markup language and data types associated with the names of the elements of the markup language; applying the elements of the markup language to the document; establishing data fields within the database for linking to corresponding markup language elements in the document; writing a unique document identifier number to the document for linking the data fields in the database to the document, wherein linking the data fields in the database to the document comprises: determining if a table associated with the document exists within a document library; if no table is associated with the document, creating a table containing user-defined elements associated with the document; selecting a table within a document library, the document library being maintained in the database where the table is associated with the document, and linking at least one markup language element in the document to corresponding data in the database; when data is entered into the database associated with the given markup language element in the document, automatically writing the data to the document in a location in the document associated with the given markup language element; when the given markup language element in the document is modified, automatically updating the corresponding data in the database; providing at least one suggested document element according to the schema file associated with the at least one intended use of the document, wherein the at least one suggested document element comprises an element structure linked to at least one corresponding data field in the database; and enforcing at least one element constraint according to the schema file, wherein the element constraint comprises at least one piece of required data for at least one document element. 12. The method of claim 1 , wherein the markup language is the Hypertext Markup Language.

28. The method of claim 25 further comprising: storing a list of exception dictionary entries, each exception dictionary entry comprising a subsequence which includes at least one lexical stroke symbol and an associated language part defining a translation of the subsequence of that exception dictionary entry; and comparing subsequences of lexical stroke symbols from the stroke symbol means, first against the dictionary entries to identify a match, said match defined as a subsequence translation, and if no match is located in the exception dictionary memory, next applying individual lexical stroke symbols to the scan chart memory to define stroke symbol translations.

28. The method of claim 25 further comprising: storing a list of exception dictionary entries, each exception dictionary entry comprising a subsequence which includes at least one lexical stroke symbol and an associated language part defining a translation of the subsequence of that exception dictionary entry; and comparing subsequences of lexical stroke symbols from the stroke symbol means, first against the dictionary entries to identify a match, said match defined as a subsequence translation, and if no match is located in the exception dictionary memory, next applying individual lexical stroke symbols to the scan chart memory to define stroke symbol translations. 33. The method of claim 28 further comprising the step of selectively altering the composition of the list of the exception dictionary entries comprising the substeps of: (a) generating a predefined first control stroke symbol, to enable alteration access to a memory in which the exception dictionary entries are stored, (b) entering a selected language part; (c) generating a predefined second control stroke symbol to define the end of the entered language part; (d) entering a plurality of lexical stroke symbols selectively defined to correspond to the entered language part; (e) generating a predefined third control stroke symbol to define the end of the entered lexical stroke symbols; and (f) storing the entered language part and the corresponding entered lexical stroke symbols in the memory in association with each other.

1. A method for register-transfer level (RTL) design checking for exploring simulation mismatches, synthesis mismatches, or ambiguous language semantics associated with generating a derived design from RTL design code, the method comprising: providing the RTL design code, to at least one processor, to generate an internal representation for verification of an electronic circuit design; comparing, by a design match engine, the RTL design code with design violation patterns contained in a design violation pattern database, wherein the design violation patterns identify violations relating to the simulation mismatches, synthesis mismatches, or ambiguous language semantics associated with generating the derived design that are otherwise undetected during equivalence checking between the derived design and the RTL design code; assigning a rule object to a design pattern in the RTL design code, by the at least one processor, when the design match engine determines that the design pattern in the RTL design code matches one of the design violation patterns in the design violation pattern database; and generating, with the at least one processor, a violation report comprising the rule objects and their corresponding design violation patterns.

1. A method for register-transfer level (RTL) design checking for exploring simulation mismatches, synthesis mismatches, or ambiguous language semantics associated with generating a derived design from RTL design code, the method comprising: providing the RTL design code, to at least one processor, to generate an internal representation for verification of an electronic circuit design; comparing, by a design match engine, the RTL design code with design violation patterns contained in a design violation pattern database, wherein the design violation patterns identify violations relating to the simulation mismatches, synthesis mismatches, or ambiguous language semantics associated with generating the derived design that are otherwise undetected during equivalence checking between the derived design and the RTL design code; assigning a rule object to a design pattern in the RTL design code, by the at least one processor, when the design match engine determines that the design pattern in the RTL design code matches one of the design violation patterns in the design violation pattern database; and generating, with the at least one processor, a violation report comprising the rule objects and their corresponding design violation patterns. 10. The method of claim 1 , wherein the method further comprises, selecting at least one of a hi-lighted unit and a hi-lighted connection corresponding to one of the rule objects in a design schematic, and graphically displaying on a display at least a portion the RTL design code in a module containing the RTL design code corresponding to the selected at least one of the hi-lighted unit and the hi-lighted connection and hi-lighting the RTL design code corresponding to the selected at least one of the hi-lighted unit and the hi-lighted connection.

14. A non-transitory computer-readable medium storing instructions, which, when executed by one or more processors, cause the one or more processors to perform: identifying first data representing an object-oriented data model, which comprises a plurality of object-oriented data model constructs for modeling object-oriented classes; wherein identifying the first data comprises identifying the first data amongst data representing an object-oriented data model; translating the first data to second data, which represents one or more YANG data model statements, but which is not extensible markup language data and which does not comprise constructs for modeling the object-oriented classes; wherein the one or more YANG data model statements capture relationships between the object-oriented data model constructs; wherein each YANG statement of the one or more YANG data model statements represented by the second data comprises a YANG keyword, which is followed by zero or more arguments, which is followed by either a semicolon or a block of YANG sub-statements enclosed within braces; and storing the second data in one or more non-transitory computer-readable media; wherein storing the second data comprises storing the second data as part of data representing a YANG data model.

14. A non-transitory computer-readable medium storing instructions, which, when executed by one or more processors, cause the one or more processors to perform: identifying first data representing an object-oriented data model, which comprises a plurality of object-oriented data model constructs for modeling object-oriented classes; wherein identifying the first data comprises identifying the first data amongst data representing an object-oriented data model; translating the first data to second data, which represents one or more YANG data model statements, but which is not extensible markup language data and which does not comprise constructs for modeling the object-oriented classes; wherein the one or more YANG data model statements capture relationships between the object-oriented data model constructs; wherein each YANG statement of the one or more YANG data model statements represented by the second data comprises a YANG keyword, which is followed by zero or more arguments, which is followed by either a semicolon or a block of YANG sub-statements enclosed within braces; and storing the second data in one or more non-transitory computer-readable media; wherein storing the second data comprises storing the second data as part of data representing a YANG data model. 15. The non-transitory computer-readable medium of claim 14 , wherein an object-oriented data model construct, from the plurality of object-oriented data model constructs, is one of: an object-oriented package definition, an object-oriented class definition, a definition of object-oriented inheritance between two object-oriented classes, a declaration of a primitive type object-oriented class instance variable of an object-oriented class, a declaration of a complex type object-oriented class instance variable of an object-oriented class, a declaration of an object-oriented class instance method of an object-oriented class, a definition of an event, or a definition of a containment relationship between two object-oriented classes.

1. A computer-implemented method for creating an intelligent bookmark to a document, comprising: displaying the document comprising at least an address, a title, and a body; receiving a user selection of content from a portion of the body of the document and user supplemented information; generating the intelligent bookmark by retrieving the address of the document, and by automatically extracting identifier information from the body of the document within the user selection; and storing the address in association with the identifier information and the user supplemented information.

1. A computer-implemented method for creating an intelligent bookmark to a document, comprising: displaying the document comprising at least an address, a title, and a body; receiving a user selection of content from a portion of the body of the document and user supplemented information; generating the intelligent bookmark by retrieving the address of the document, and by automatically extracting identifier information from the body of the document within the user selection; and storing the address in association with the identifier information and the user supplemented information. 6. The method of claim 1 , further comprising: notifying the user of web sites of interest based on the identifier information.

4. The apparatus of claim 3 , wherein the labeling each pixel further comprises the processor configured to: determine if each pixel in the image represents an edge; estimate distribution of edge density in a neighborhood of the each pixel in the image; and use the distribution to determine a descriptive type of each pixel and labeling each pixel accordingly.

4. The apparatus of claim 3 , wherein the labeling each pixel further comprises the processor configured to: determine if each pixel in the image represents an edge; estimate distribution of edge density in a neighborhood of the each pixel in the image; and use the distribution to determine a descriptive type of each pixel and labeling each pixel accordingly. 11. The apparatus of claim 4 , wherein the smoothing is performed only on perceptually significant pixels.

1. A method, comprising operating a computer processor to perform operations comprising: accessing process execution data derived from audit logs generated during execution of instances of an automated business process comprising business process entities and defined by a directed graph of interconnected nodes representing respective activities, wherein the business process entities comprise services and resources, and each of the activities is defined by a respective service and is performed by a respective set of one or more resources; classifying the instances of the business process in accordance with a quality taxonomy defined by a set of quality categories each of which is associated with a respective condition on the process execution data and a respective quality score value, wherein the classifying comprises assigning the business process instances to respective ones of the quality categories based on application of the respective conditions to the accessed process execution data; building a predictive model comprising a respective set of predictive rules for each of multiple logical partitions of the directed graph defining the business process based upon the assignment of the business process instances to respective ones of the categories, wherein each of the predictive rules assigns a respective probability to each of the quality categories of the quality taxonomy for each of the business process entities that are invocable at the respective logical partitions of the directed graph, and each of the probabilities represents a likelihood that a given business process instance that invokes the respective business process entity at the respective logical partition of the directed graph will have an outcome corresponding to the respective quality category; and executing an active instance of the business process based on the predictive model.

1. A method, comprising operating a computer processor to perform operations comprising: accessing process execution data derived from audit logs generated during execution of instances of an automated business process comprising business process entities and defined by a directed graph of interconnected nodes representing respective activities, wherein the business process entities comprise services and resources, and each of the activities is defined by a respective service and is performed by a respective set of one or more resources; classifying the instances of the business process in accordance with a quality taxonomy defined by a set of quality categories each of which is associated with a respective condition on the process execution data and a respective quality score value, wherein the classifying comprises assigning the business process instances to respective ones of the quality categories based on application of the respective conditions to the accessed process execution data; building a predictive model comprising a respective set of predictive rules for each of multiple logical partitions of the directed graph defining the business process based upon the assignment of the business process instances to respective ones of the categories, wherein each of the predictive rules assigns a respective probability to each of the quality categories of the quality taxonomy for each of the business process entities that are invocable at the respective logical partitions of the directed graph, and each of the probabilities represents a likelihood that a given business process instance that invokes the respective business process entity at the respective logical partition of the directed graph will have an outcome corresponding to the respective quality category; and executing an active instance of the business process based on the predictive model. 2. The method of claim 1 , wherein the building comprises partitioning the directed graph into the logical partitions.

10. A system comprising: a processor; a computer-readable storage medium coupled to the processor, the computer-readable storage medium including instructions that, when executed by a processor, cause the processor to: receive a request for information based on an instruction in a markup language document, wherein the request for information is responsive to a request for a web page of a third-party website that is within a domain of a third-party website that is different from a domain of a social networking system; identify a user associated with the request; determine the requested information based on social information associated with the user, wherein the requested information comprises a set of content items that relate to (1) one or more actions performed by one or more other users with whom the user has established a connection in the social networking system, and (2) at least one specified URL or domain; and send the determined requested information to a client device for rendering as content personalized for the user for display within the rendered web page.

10. A system comprising: a processor; a computer-readable storage medium coupled to the processor, the computer-readable storage medium including instructions that, when executed by a processor, cause the processor to: receive a request for information based on an instruction in a markup language document, wherein the request for information is responsive to a request for a web page of a third-party website that is within a domain of a third-party website that is different from a domain of a social networking system; identify a user associated with the request; determine the requested information based on social information associated with the user, wherein the requested information comprises a set of content items that relate to (1) one or more actions performed by one or more other users with whom the user has established a connection in the social networking system, and (2) at least one specified URL or domain; and send the determined requested information to a client device for rendering as content personalized for the user for display within the rendered web page. 16. The system of claim 10 , wherein the actions include at least one of: specifying a connection to a web page in connection with the social networking system, sharing the web page in connection with the social networking system, and posting a comment regarding a web page in connection with the social networking system.

1. At a computer system, a method for adding an executable contract to a method such that when the method is called, the executable contract is also executed to ensure that the method is called according to one or more rules specified in the contract, the method comprising: an act of receiving source code that includes a call to a method of a first class as well as a call to a method of a contract class, the contract class providing methods for ensuring that the call to the method of the first class is performed in accordance with one or more rules specified as input to the call to the method of the contract class; an act of compiling the received source code into intermediate language code that includes code for implementing the method of the first class as well as code for implementing the method of the contact class which is configured to be executed to verify that the one or more rules are met whenever the method of the first class is called and to report whether the one or more rules are met; an act of storing the intermediate language code; an act of executing the intermediate language code, wherein when the method of the first class is executed, the method of the contract class is also executed to verify whether the one or more rules are met; and an act of reporting to a user via a user interface whether the one or more rules were met during the execution of the method of the first class.

1. At a computer system, a method for adding an executable contract to a method such that when the method is called, the executable contract is also executed to ensure that the method is called according to one or more rules specified in the contract, the method comprising: an act of receiving source code that includes a call to a method of a first class as well as a call to a method of a contract class, the contract class providing methods for ensuring that the call to the method of the first class is performed in accordance with one or more rules specified as input to the call to the method of the contract class; an act of compiling the received source code into intermediate language code that includes code for implementing the method of the first class as well as code for implementing the method of the contact class which is configured to be executed to verify that the one or more rules are met whenever the method of the first class is called and to report whether the one or more rules are met; an act of storing the intermediate language code; an act of executing the intermediate language code, wherein when the method of the first class is executed, the method of the contract class is also executed to verify whether the one or more rules are met; and an act of reporting to a user via a user interface whether the one or more rules were met during the execution of the method of the first class. 6. The method of claim 1 , wherein the one or more rules include one or more of the following: preconditions, postconditions, lock declarations, checked exceptions, usage protocols and object invariants.

4. The method of claim 1 , further comprising: identifying a set of query suggestions for the partial query; and modifying the set of query suggestions to include the query completion template, and wherein providing for display the query completion template includes providing for display the modified set of query suggestions.

4. The method of claim 1 , further comprising: identifying a set of query suggestions for the partial query; and modifying the set of query suggestions to include the query completion template, and wherein providing for display the query completion template includes providing for display the modified set of query suggestions. 5. The method of claim 4 , further comprising maintaining a database of query completion templates, wherein each query completion template in the database is associated with a list of terms corresponding to a category of information; identifying query terms within the set of query suggestions; and selecting the query completion template for display from the database of query completion templates based on one or more of the query terms within the set of query suggestions appearing within the list of terms associated with the selected query completion template.

10. A method comprising: displaying information regarding one or more files of a file system in a window of a graphical user interface; first detecting, while the information regarding the one or more files is displayed in the window, a user selection of at least one particular file of the one or more files; second detecting, following the first detecting of the user selection and while the information regarding the one or more files is displayed in the window, an input from the user of one or more characters, each of the one or more characters in the input from the user corresponding to successive characters of a desired tag to be applied to the at least one particular file; in response to the first detecting of the selection of the at least one particular file and the second detecting of the input of the one or more characters, following the first detecting, automatically initiating a tagging mode of the graphical user interface without further user interaction, other than the user selection and the input of the one or more characters from the user, displaying an indication that tagging is active, upon initiating the tagging mode, the desired tag being one of a plurality of different tags to be applied to the at least one particular file via the tagging mode, all of the plurality of different tags having coexisting associations, as coexistent tags, with the at least one particular file, and determining at least one suggested tag based at least in part on the input from the user, the at least one suggested tag including at least some of the input; and displaying in the window the at least one suggested tag to be applied to the at least one particular file.

10. A method comprising: displaying information regarding one or more files of a file system in a window of a graphical user interface; first detecting, while the information regarding the one or more files is displayed in the window, a user selection of at least one particular file of the one or more files; second detecting, following the first detecting of the user selection and while the information regarding the one or more files is displayed in the window, an input from the user of one or more characters, each of the one or more characters in the input from the user corresponding to successive characters of a desired tag to be applied to the at least one particular file; in response to the first detecting of the selection of the at least one particular file and the second detecting of the input of the one or more characters, following the first detecting, automatically initiating a tagging mode of the graphical user interface without further user interaction, other than the user selection and the input of the one or more characters from the user, displaying an indication that tagging is active, upon initiating the tagging mode, the desired tag being one of a plurality of different tags to be applied to the at least one particular file via the tagging mode, all of the plurality of different tags having coexisting associations, as coexistent tags, with the at least one particular file, and determining at least one suggested tag based at least in part on the input from the user, the at least one suggested tag including at least some of the input; and displaying in the window the at least one suggested tag to be applied to the at least one particular file. 15. The method of claim 10 , wherein determining the at least one suggested tag comprises determining a first suggested tag that includes all of the one or more characters of the input from the user.

12. At least one computer-readable storage medium having computer-executable instructions stored thereon which, when executed by a computer system, cause the computer system to perform a method comprising: receiving at least one natural language input query; defining a plurality of term occurrence-level constraints applicable to the input query; decomposing the input query into a plurality of sub-queries, wherein a first sub-query relates to a first fact requested in the input query, and wherein at least a second sub-query relates to a second fact requested in the input query; instantiating a first iterator to identify at least a first candidate fact that is responsive to the input query and that relates to the first fact; instantiating at least a second iterator to identify at least a first candidate fact that is responsive to the input query and that relates to the second fact; traversing the first and second iterators through at least a portion of a fact index; receiving the first candidate fact from the first iterator, wherein the first candidate fact includes at least a first term occurrence; receiving the second candidate fact from the second iterator, wherein the second candidate fact includes at least a second term occurrence; performing at least one fact-level operation on at least the first and second candidate facts; and selecting search results for the input query, based at least in part on performing the fact-level operation on the candidate facts.

12. At least one computer-readable storage medium having computer-executable instructions stored thereon which, when executed by a computer system, cause the computer system to perform a method comprising: receiving at least one natural language input query; defining a plurality of term occurrence-level constraints applicable to the input query; decomposing the input query into a plurality of sub-queries, wherein a first sub-query relates to a first fact requested in the input query, and wherein at least a second sub-query relates to a second fact requested in the input query; instantiating a first iterator to identify at least a first candidate fact that is responsive to the input query and that relates to the first fact; instantiating at least a second iterator to identify at least a first candidate fact that is responsive to the input query and that relates to the second fact; traversing the first and second iterators through at least a portion of a fact index; receiving the first candidate fact from the first iterator, wherein the first candidate fact includes at least a first term occurrence; receiving the second candidate fact from the second iterator, wherein the second candidate fact includes at least a second term occurrence; performing at least one fact-level operation on at least the first and second candidate facts; and selecting search results for the input query, based at least in part on performing the fact-level operation on the candidate facts. 14. The storage medium of claim 12 , further comprising instructions for instantiating at least a third iterator to perform the at least one fact-level operation on at least the first and second candidate facts, wherein the third iterator is for communicating with the first and second iterators.

1. A method of speech recognition processing comprising: in an automatic speech recognition engine: producing with a pattern matching recognizer an N-best list of recognition hypotheses corresponding to a spoken input; and rescoring the hypotheses in the automatic speech recognition engine to produce a rescored N-best list output from the automatic speech recognition engine; wherein the rescoring uses a plurality of rescoring categories based on position in the rescored N-best list such that some positions in the rescored N-best list are rescored based on a first combination of rescoring categories and other positions in the rescored N-best list are rescored based on a second combination of rescoring categories.

1. A method of speech recognition processing comprising: in an automatic speech recognition engine: producing with a pattern matching recognizer an N-best list of recognition hypotheses corresponding to a spoken input; and rescoring the hypotheses in the automatic speech recognition engine to produce a rescored N-best list output from the automatic speech recognition engine; wherein the rescoring uses a plurality of rescoring categories based on position in the rescored N-best list such that some positions in the rescored N-best list are rescored based on a first combination of rescoring categories and other positions in the rescored N-best list are rescored based on a second combination of rescoring categories. 11. A method according to claim 1 , wherein the recognition hypotheses represent place names for a navigation system.

1. A computer-implemented method of matching users to other users, the method comprising: storing, in computer storage, event data comprising order data reflective of items ordered from an electronic catalog by each of a plurality of users; programmatically generating a score that reflects a degree to which item preferences of a first user of said plurality of users are similar to item preferences of a second user of said plurality of users, said score taking into consideration a first plurality of items ordered by the first user and a second plurality of items ordered by the second user, wherein generating the score comprises weighting a first item and a second item identified in both the first and second plurality of items, wherein the first and second items are different, wherein the first and second items are weighted differently based at least in part on a first inherent characteristic of the first item and a second inherent characteristic of the second item, wherein the first and second inherent characteristics are different, and wherein generating the score further comprises taking into consideration at least one additional type of event data reflective of user affinities for items represented in the electronic catalog; and based at least in part on the score, programmatically determining whether to recommend the second user to the first user.

1. A computer-implemented method of matching users to other users, the method comprising: storing, in computer storage, event data comprising order data reflective of items ordered from an electronic catalog by each of a plurality of users; programmatically generating a score that reflects a degree to which item preferences of a first user of said plurality of users are similar to item preferences of a second user of said plurality of users, said score taking into consideration a first plurality of items ordered by the first user and a second plurality of items ordered by the second user, wherein generating the score comprises weighting a first item and a second item identified in both the first and second plurality of items, wherein the first and second items are different, wherein the first and second items are weighted differently based at least in part on a first inherent characteristic of the first item and a second inherent characteristic of the second item, wherein the first and second inherent characteristics are different, and wherein generating the score further comprises taking into consideration at least one additional type of event data reflective of user affinities for items represented in the electronic catalog; and based at least in part on the score, programmatically determining whether to recommend the second user to the first user. 2. The method of claim 1 , wherein the score reflects a degree to which the first plurality of items and the second plurality of items are related.

9. A computer readable storage medium containing a program which, when executed, performs an operation, comprising: receiving a database query; determining at least one data element required for executing the database query; identifying, from a plurality of storage devices, a storage device storing the determined at least one data element; adding the received database query to a first queue of a plurality of queues each having a plurality of queued queries, each of the queues having a predefined association with a respective storage device of the plurality of storage devices, wherein the predefined association of the first queue is a first predefined association with the identified storage device storing the determined at least one data element, wherein each of the queued queries of a given of one of the queues requires one or more data elements stored in the respective storage device in order to be executed, wherein each queued query is received during a specified time period, and wherein the specified time period is selected according to an energy consumption objective; and after the specified time period: dispatching the plurality of queued queries from the first queue; retrieving, from the respective storage device, data elements required for executing the dispatched queries; and executing the dispatched queries, using the retrieved data elements as inputs.

9. A computer readable storage medium containing a program which, when executed, performs an operation, comprising: receiving a database query; determining at least one data element required for executing the database query; identifying, from a plurality of storage devices, a storage device storing the determined at least one data element; adding the received database query to a first queue of a plurality of queues each having a plurality of queued queries, each of the queues having a predefined association with a respective storage device of the plurality of storage devices, wherein the predefined association of the first queue is a first predefined association with the identified storage device storing the determined at least one data element, wherein each of the queued queries of a given of one of the queues requires one or more data elements stored in the respective storage device in order to be executed, wherein each queued query is received during a specified time period, and wherein the specified time period is selected according to an energy consumption objective; and after the specified time period: dispatching the plurality of queued queries from the first queue; retrieving, from the respective storage device, data elements required for executing the dispatched queries; and executing the dispatched queries, using the retrieved data elements as inputs. 15. The computer readable storage medium of claim 9 , wherein the at least one data element comprises at least one of: (i) a table of a database, and (ii) an index of a database.

16. A computer-implemented method to assist a human analyst in analyzing large amounts of electronic communications for malfeasance, comprising: by one or more computer processors configured to execute software modules comprising computer executable instructions: designating one or more seeds by: accessing a plurality of transaction risk indicators and at least one electronic trade data item from a plurality of electronic trade data items, each electronic trade data item associated with a trade of a financial instrument, properties, and property values, each electronic trade data item comprising a trader property associating a trader identifier of a trader executing the trade; comparing the plurality of transaction risk indicators to the at least one electronic trade data item and associated properties; based at least on the comparison and in response to determining the at least one electronic trade data item is related to at least one transaction risk indicator, designating the at least one electronic trade data item as a first seed; accessing, from one or more computer readable storage devices, a plurality of electronic communication data items, each electronic communication data item associated with an electronic communication and one or more trader identifiers that sent or received the electronic communication; determining a subset of electronic communication data items from the plurality of electronic communication data items that are identifiable as likely side conversations, wherein determining the subset of electronic communication data items comprises identifying an electronic communication data item that has at least one less of a particular participant than a previous electronic communication associated with the electronic communication data item; searching the subset of electronic communication data items to identify an initial electronic communication data item, distinct from the at least one electronic trade data item, based at least on a communication risk indicator of a plurality of communication risk indicators and a sender or recipient of the initial electronic communication data item corresponding to a trader associated with the at least one electronic trade data item; and designating the initial electronic communication data item as a second seed; for each designated first and second seed: identifying one or more first data items determined to be associated with the first seed based at least in part on a first clustering strategy of a plurality of clustering strategies, wherein the first clustering strategy queries one or more cluster data sources to determine at least one of: a first trader identifier associated with the first seed of the at least one electronic trade data item, or an electronic communication data item of the plurality of electronic communication data items associated with a first trader corresponding to the first trader identifier; identifying one or more second data items determined to be associated with the second seed based at least in part on a second clustering strategy of the plurality of clustering strategies, wherein the second clustering strategy queries the one or more cluster data sources to determine at least one of: a sender or recipient associated with the second seed of the initial electronic communication data item, an initial participant corresponding to at least one of the sender or recipient of the initial electronic communication data item, or an electronic trade data item of the plurality of electronic trade data items associated with the initial participant; generating a cluster based at least on the first and second seed, wherein generating the cluster comprises: adding the first and second seed to the cluster; adding the one or more first data items to the cluster; adding the one or more second data items to the cluster; storing the generated cluster in the one or more computer readable storage devices; and determining a score for the generated cluster, based at least on one or more scoring criterions; and causing presentation of at least one generated cluster and the determined score for the at least one generated cluster in a user interface of a client computing device.

16. A computer-implemented method to assist a human analyst in analyzing large amounts of electronic communications for malfeasance, comprising: by one or more computer processors configured to execute software modules comprising computer executable instructions: designating one or more seeds by: accessing a plurality of transaction risk indicators and at least one electronic trade data item from a plurality of electronic trade data items, each electronic trade data item associated with a trade of a financial instrument, properties, and property values, each electronic trade data item comprising a trader property associating a trader identifier of a trader executing the trade; comparing the plurality of transaction risk indicators to the at least one electronic trade data item and associated properties; based at least on the comparison and in response to determining the at least one electronic trade data item is related to at least one transaction risk indicator, designating the at least one electronic trade data item as a first seed; accessing, from one or more computer readable storage devices, a plurality of electronic communication data items, each electronic communication data item associated with an electronic communication and one or more trader identifiers that sent or received the electronic communication; determining a subset of electronic communication data items from the plurality of electronic communication data items that are identifiable as likely side conversations, wherein determining the subset of electronic communication data items comprises identifying an electronic communication data item that has at least one less of a particular participant than a previous electronic communication associated with the electronic communication data item; searching the subset of electronic communication data items to identify an initial electronic communication data item, distinct from the at least one electronic trade data item, based at least on a communication risk indicator of a plurality of communication risk indicators and a sender or recipient of the initial electronic communication data item corresponding to a trader associated with the at least one electronic trade data item; and designating the initial electronic communication data item as a second seed; for each designated first and second seed: identifying one or more first data items determined to be associated with the first seed based at least in part on a first clustering strategy of a plurality of clustering strategies, wherein the first clustering strategy queries one or more cluster data sources to determine at least one of: a first trader identifier associated with the first seed of the at least one electronic trade data item, or an electronic communication data item of the plurality of electronic communication data items associated with a first trader corresponding to the first trader identifier; identifying one or more second data items determined to be associated with the second seed based at least in part on a second clustering strategy of the plurality of clustering strategies, wherein the second clustering strategy queries the one or more cluster data sources to determine at least one of: a sender or recipient associated with the second seed of the initial electronic communication data item, an initial participant corresponding to at least one of the sender or recipient of the initial electronic communication data item, or an electronic trade data item of the plurality of electronic trade data items associated with the initial participant; generating a cluster based at least on the first and second seed, wherein generating the cluster comprises: adding the first and second seed to the cluster; adding the one or more first data items to the cluster; adding the one or more second data items to the cluster; storing the generated cluster in the one or more computer readable storage devices; and determining a score for the generated cluster, based at least on one or more scoring criterions; and causing presentation of at least one generated cluster and the determined score for the at least one generated cluster in a user interface of a client computing device. 20. The computer-implemented method of claim 16 , wherein an additional electronic communication data item from the plurality of electronic communication data items comprises an email message or a chat message, and wherein generating the cluster further comprises: identifying the additional electronic communication data item, from the plurality of electronic communication data items, comprising a name of an entity and recipient data, wherein the recipient data indicates that a recipient of the additional electronic communication data item matches a person or identifier value in the cluster, and a representative of the entity was not a recipient of the additional electronic communication data item.

11. A computer program product as recited in claim 10 , further including the view object identifying an object tag that includes a URI of a television resource.

11. A computer program product as recited in claim 10 , further including the view object identifying an object tag that includes a URI of a television resource. 15. A computer program product as recited in claim 11 , wherein the view object causes a television broadcast to be displayed in response to the URI identifying the television resource.

12. The computer-readable storage memory of claim 10 , comprising further computer-executable instructions, which when executed cause the at least one processor to: provide an interface for an extension to a compiler or language service.

12. The computer-readable storage memory of claim 10 , comprising further computer-executable instructions, which when executed cause the at least one processor to: provide an interface for an extension to a compiler or language service. 13. The computer-readable storage memory of claim 12 , comprising further computer-executable instructions, which when executed cause the at least one processor to: provide an extensible compiler or extensible language service.

1. A method on a mobile device of providing keyword-based services to message recipients, the method comprising: receiving a text message at a mobile device of a user; identifying a plurality of keywords in the received text message, wherein at least some of the plurality of keywords are identified by comparing words in the text message with a keyword inventory that is maintained on the mobile device; retrieving a market segment of the user, the market segment determined based on observed user activity on the mobile device and further based on profiling messages received by the user; selecting a subset of the identified keywords based on user-specific information, including the market segment of the user, stored on the mobile device; displaying the received text message to the user of the mobile device, wherein the text of the displayed message is formatted to distinguish the subset of identified keywords in the text from non-identified keyword text in the displayed message; associating each of the subset of identified keywords with at least one advertisement and at least one contextual service; receiving a selection of an identified keyword in the displayed message by the user; displaying to the user at least one advertisement and at least one contextual service associated with the selected keyword; receiving a selection of a displayed advertisement or contextual service from the user; invoking the selected advertisement or contextual service to request additional information associated with the keyword, wherein invoking the selected advertisement or contextual service comprises transmitting the user-specific information to the selected advertisement or contextual service provided that the user has authorized such transmittal; and receiving information associated with the keyword from the selected advertisement or contextual service and presenting the received information to the user.

1. A method on a mobile device of providing keyword-based services to message recipients, the method comprising: receiving a text message at a mobile device of a user; identifying a plurality of keywords in the received text message, wherein at least some of the plurality of keywords are identified by comparing words in the text message with a keyword inventory that is maintained on the mobile device; retrieving a market segment of the user, the market segment determined based on observed user activity on the mobile device and further based on profiling messages received by the user; selecting a subset of the identified keywords based on user-specific information, including the market segment of the user, stored on the mobile device; displaying the received text message to the user of the mobile device, wherein the text of the displayed message is formatted to distinguish the subset of identified keywords in the text from non-identified keyword text in the displayed message; associating each of the subset of identified keywords with at least one advertisement and at least one contextual service; receiving a selection of an identified keyword in the displayed message by the user; displaying to the user at least one advertisement and at least one contextual service associated with the selected keyword; receiving a selection of a displayed advertisement or contextual service from the user; invoking the selected advertisement or contextual service to request additional information associated with the keyword, wherein invoking the selected advertisement or contextual service comprises transmitting the user-specific information to the selected advertisement or contextual service provided that the user has authorized such transmittal; and receiving information associated with the keyword from the selected advertisement or contextual service and presenting the received information to the user. 15. The method of claim 1 , wherein transmitting the user-specific information to the selected advertisement or contextual service includes transmitting information identifying the market segment of the user.

13. The method of claim 1 further comprising training the document analysis system to automatically classify each document into a corresponding document category and to automatically organize each job according to the document categories it contains.

13. The method of claim 1 further comprising training the document analysis system to automatically classify each document into a corresponding document category and to automatically organize each job according to the document categories it contains. 14. The method of claim 13 wherein extracting the binarized text and binarized image features from the received binarized document comprises recognizing, by the server, the binarized text and binarized image features of the received binarized document as part of a workflow.

4. The computer readable storage medium of claim 1 further comprising executable instructions to convert dimensions and measures to a reduced form.

4. The computer readable storage medium of claim 1 further comprising executable instructions to convert dimensions and measures to a reduced form. 7. The computer readable storage medium of claim 4 wherein the executable instructions to convert the dimensions and the measures to the reduced form include executable instructions to replace references to data model objects by name with index values.

1. At least one non-transitory machine-readable medium comprising a set of instructions that, in response to being executed on a computing device, cause the computing device to: identify, in response to a triggering event associated with a workload of a storage system, a proposed solution comprising one or more actions to be performed; predict, for the proposed solution, a value of an output metric for the workload using a mapping function for the output metric based on a plurality of input metric values for a foreground workload and a plurality of input metric values for a set of background workloads of the storage system, the mapping function produced using a machine learning algorithm; and generate an evaluation value for the proposed solution based on the predicted value of the output metric.

1. At least one non-transitory machine-readable medium comprising a set of instructions that, in response to being executed on a computing device, cause the computing device to: identify, in response to a triggering event associated with a workload of a storage system, a proposed solution comprising one or more actions to be performed; predict, for the proposed solution, a value of an output metric for the workload using a mapping function for the output metric based on a plurality of input metric values for a foreground workload and a plurality of input metric values for a set of background workloads of the storage system, the mapping function produced using a machine learning algorithm; and generate an evaluation value for the proposed solution based on the predicted value of the output metric. 2. The at least one non-transitory machine-readable medium of claim 1 , comprising a set of instructions that, in response to being executed on the computing device, cause the computing device to: predict, for the proposed solution, respective values of each of a plurality of output metrics for the workload using respective mapping functions for each of the plurality of output metrics; and generate the evaluation value for the proposed solution based on the respective predicted values of each of the plurality of output metrics.

13. The system of claim 12 , wherein the system is further configured to: determine an implicit intent and an explicit intent.

13. The system of claim 12 , wherein the system is further configured to: determine an implicit intent and an explicit intent. 15. The system of claim 13 , wherein the system is further configured to: tokenize the input search query to at least one token to create at least one tokenized query; process the at least one tokenized query by a plurality of engines, wherein each engine of the plurality of engines configured to compute a certainty score that indicates a probability that the at least one tokenized query is mapped to at least one entity, wherein each engine of the plurality of engines is further configured with at least one entity indicating a topic of interest, thereby the plurality of engines are configured with different entities; receive from a set of engines of the plurality of engines their respective entities and computed certainty scores, wherein the set of engines output computed certainty scores above a predefined threshold; and analyze the received certainty scores and the respective entities to determine the search intent.

1. Computer-readable media embodying instructions executable by a computer to perform a method comprising: receiving, from a user interface, an annotation associated with a background image; adding the annotation to a queue of pending annotations; causing transmission of the annotation to a server; removing the annotation from the queue of pending annotations, and adding the annotation to a list of acknowledged annotations, when an acknowledgment of the annotation is received from the server; and generating a display image comprising the background image, annotations in the list of acknowledged annotations, and annotations in the queue of pending annotations.

1. Computer-readable media embodying instructions executable by a computer to perform a method comprising: receiving, from a user interface, an annotation associated with a background image; adding the annotation to a queue of pending annotations; causing transmission of the annotation to a server; removing the annotation from the queue of pending annotations, and adding the annotation to a list of acknowledged annotations, when an acknowledgment of the annotation is received from the server; and generating a display image comprising the background image, annotations in the list of acknowledged annotations, and annotations in the queue of pending annotations. 3. The computer-readable media of claim 1 , wherein generating the display image comprises: rendering the annotations in the list of acknowledged annotations with a first appearance; and rendering the annotations in the queue of pending annotations with a second appearance.