Patent Application: US-31715308-A

Abstract:
an apparatus consisting of a networked data storage device and a software system for automatically indexing the data on the storage device by topic , and for sharing both the data and the topic indexes on the network . these may be used to provide the user of the apparatus with functionality including , but not limited to , content - based searching , sharing and automatic recommendations . the storage device stores its data by means of a file system . the software executes either on co - processing hardware within the storage device enclosure or on some other device on the network . the indexing portion of the software performs topic - based categorization by means of a higher - order path analysis algorithm , which mimics human intuition by considering both high - and low - order links between data elements . the indexes generated by the software are automatically partitioned by topic . the indexing software operates continuously and transparently , updating the index as data is added to or removed from the storage device . the software includes a novel mesh - based , self - healing peer - to - peer protocol that has been specialized to facilitate sharing of both topic indexes and the stored data on the network . the software implements user authentication and user - level access control to data and indexes . the search , retrieval and recommendation functions are operated through a graphical user interface on a device that may be either local or remote to the storage device . the apparatus is well suited to function as an independent node on peer - to - peer communications network .

Description:
following is a description of all the hardware and software components of the invention and how they interoperate . the novel implementations of the components , as well as the novel means of interconnection , provide an integrated functionality that is an improvement over the prior art in the domains of data search and sharing . the preferred embodiment of the invention is one in which the peer - to - peer and indexing software components reside on integrated circuits mounted to a printed circuit board within the housing containing the data storage device itself . in the preferred embodiment , the data storage device consists of a commonly available hard disk drive , and will be referred to from here on as the “ hard drive ”. in this embodiment , a version of the user interface software is also implemented in the integrated circuits within the housing , though this embodiment also has the capability of interfacing with user interface software residing on external devices . a file system is a specification of a method for storing , arranging , and retrieving data on a data storage device in the form of files and descriptive metadata accompanying those files . all modern file systems provide a hierarchical directory structure for organizing data on the storage device in the form of files . the invention contains a software component implementing a transaction - based file system for storing and retrieving data on the hard disk component . the nature of the transaction - based file system allows for multiple hosts to read and write to the hard disk simultaneously . this solves many problems related to sharing the contents of the hard disk on a network . in addition , this file system provides group - level security through acls ( access control lists ). this means that there can be fine - grained controls at the file system level over which users , by group , have access to each file and directory on the disk . read , write , and execute permissions can be granted independently to different groups . the function of the indexing module is to examine data files and create an index of those data files by topic . the topic should be an accurate classification of the contents of the data file within the range of a pre - existing set of topic categories . there are several methods for examining the data that is to be written to the disk . in one embodiment of the indexing software , files are examined for data in the form of plain text , and then that text is extracted and used to determine a topic using algorithms from the data mining field . one such state - of - the - art classification method is implemented , which receives additional input from the system &# 39 ; s user interface . upon installation of the system , the system scans the data files that the user wishes to share , randomly selects a sample of the files , and prompts the user to input the correct topic of those files . this sample of user - labeled data is referred to in the field of statistical machine learning as the “ training set .” an algorithm known as the “ training algorithm ” uses the user - labeled data as its training set , and generates a second algorithm , called the “ classification rule ”, for automatically classifying data that will be written to the disk in the future . the combination of training algorithm and classification rule make up our classification method . typically they are not referred to independently , but as one statistical machine learning algorithm . the classification method used for this invention falls in the realm of statistical machine learning algorithms , yet includes key advancements in the field . statistical machine learning algorithms operate on flat data and traditionally assume that instances are independent and identically distributed ( iid ). however , this context - free approach does not exploit the available information about relationships between instances in the dataset [ 4 ]. in link mining , a subset of the field of statistical relational learning , algorithms operate on relational data that includes explicit links between instances . these relations provide rich information that can be used to improve classification accuracy of learned models , since attributes of linked instances are often correlated , and links are more likely to exist between instances that have some commonality . given a set of test instances , relational models simultaneously label all instances in order to exploit the correlations between class labels of related instances . this is also known as collective classification ( or collective inference ), and violates the traditional independence assumption . several studies ( e . g ., [ 5 ], [ 6 ], [ 7 ]) have shown , however , that by making inferences about multiple data instances simultaneously , collective inference can significantly reduce classification error [ 9 ]. the base classification algorithm used by the invention is the well - known naïve bayes algorithm . naïve bayes is commonly used in text classification because it executes quickly [ 10 ]. the naïve bayes classifier is the simplest of bayesian classifiers in that it assumes that all attributes of the examples are independent of each other given the context of the class . although this assumption does not hold for most real - world datasets , overall naïve bayes performs fairly well . traditional ( or first - order ) naïve bayes uses documents as instances and words as the attributes . this maps directly to the method of our invention , in that the data files written to the hard disk are the documents , and the textual contents of those files , consisting either of words or of character n - grams , are the attributes . to use the naïve bayes algorithm with a set of documents , first an ordered set of attributes ( words or n - grams ) is selected ( commonly the union of all words found in a corpus of documents .) the documents are then used to create a set of training vectors , one vector for each document . the length of the vectors is equal to the size of the set of entities used in the classification process , and each coordinate t of the vector is either 1 or 0 indicating whether that word is present in the document . to each vector is appended its class c , representing the true class label of this document . the training process for naïve bayes is at heart a probabilistic calculation using the well - known bayes &# 39 ; rule . based on the training vectors , the following set of empirical probabilities can be calculated : p ( t | c )=(# of documents in class c including word t )/(# of documents in class c ) ( 1 ) p ( c )=(# of documents in class c )/( total # of documents ) ( 2 ) the training algorithm consists entirely of computing these quantities from the given vectors . after these probabilities are calculated , bayes &# 39 ; rule gives us a rule for calculating the probability that any future encountered document d belongs to class c : so to classify any future document , this probability is calculated for all classes , and the label of the class whose probability is highest is selected as the correct label . the training algorithm for this invention has been modified to include the higher - order relational information described above , overcoming the independence assumption . in our system , the higher - order information used is in the form of a second - order co - occurrence path . explicit links described above can take the form of words or n - grams in common between documents . for example , if two documents share the same term , those two documents have a first - order link to each other . if two documents both have a first - order link to a third document , but not to each other , then that is a second - order link , and so on . the highest order of links to be used in the training process is fixed beforehand . the preferred embodiment of this invention uses a second - order model . in the higher - order algorithm , the form of the training set used is identical to that of traditional naïve bayes . however , a different set of empirical probabilities is calculated , as follows : p − ( t | c )=(# of higher - order paths in class c including word t )/(# of higher - order paths in class c ) ( 4 ) p ( c )=(# of higher - order paths in class c )/( total # of higher - order paths ) the rest of the calculations and the inference process are the same as traditional ( first - order ) naïve bayes . though the training algorithm considers higher - order information , after the training phase is complete the classification is still done with single data instances . in other words , after ( 4 ) and ( 5 ) are calculated from the training set , it is possible to compute ( 3 ) with a single document in order to perform the classification . this is a significant advantage as it allows data files to be classified one - by - one , in real time , as they are written to the hard disk . computing the higher - order statistics used for the training process requires enumeration of all the second - order co - occurrence paths . this can be done using methods of graph theory . our definition of a higher - order path is akin to that found in graph theory , which states that given a non - empty graph g =( v , e ) of the form v ={ x 0 , x 1 , . . . , x k }, e ={ x 0 x 1 , x 1 x 2 , . . . , x k - 1 x k } with nodes x i distinct , two vertices x i and x k are linked by a path p where the number of edges in p is its length . such a path is often referred to by the natural sequence of its vertices x 0 x 1 . . . x k . [ 6 ]. our definition differs from this in a couple of respects , however . first , vertices v ={ e 0 , e 1 , . . . , e k } represent entities , and edges e ={ r 0 , r 1 , . . . , r m } represents records , documents or instances . several edges may exist between given entities . finally and most importantly , in a higher - order path both vertices and edges must be distinct . co - occurrence relations in a record or instance set can be represented as an undirected graph g =( v , e ) such that v is a finite set of vertices ( i . e ., entities ) and e is the set of edges representing co - occurrence relations . in other words , if two entities co - oocur in a record then there is an edge between the corresponding vertices and this edge is labeled with the records ( s ) in which they co - occur . it is not straightforward , however , to depict higher - order paths with conventional graph structures because multiple paths may connect two given entities : for example , e 1 - r 1 - e 2 and e 1 - r 2 - e 2 are both valid paths between entities e 1 and e 2 . a conventional graph can nonetheless be modified to represent paths of this nature by maintaining a data structure that contains lists of records for each edge . we term this a path group . path groups are extracted directly from the co - occurrence graph g . using this representation , the higher - order paths correspond to a complete matching in the bipartite graph formed from the set of entities and the set of lists of records . likewise , higher - order paths defined in this manner are the system of distinct representatives of the sets of records for each edge . using the path group representation , we need to identify the systems of distinct representatives ( sdrs ) of the record sets . each distinct representative in the path group satisfies the second requirement ( i . e ., edges must be unique ) and corresponds to a higher order path . in order to enumerate all the distinct representatives in a given path group , a bipartite graph g b =( v 1 ∪ v 2 , e ) is formed such that v 1 is the sets of records ( s 1 , s 2 , . . . ) in a given path group and v 2 is the records themselves . a maximum matching with cardinality | v 1 | in this bipartite graph yields the sdr for the higher order path . this process is summarized in drawing 7 . in drawing 7 , we can see an example second - order path group ( e 1 -{ 1 , 2 }- e 2 -{ 1 , 2 , 4 }- e 3 ) that is extracted from the co - occurrence graph g c . this particular 2 nd order path group includes two sets of records : s 1 ={ 1 , 2 } and s 2 ={ 1 , 2 , 4 }. s 1 corresponds to the records in which e 1 and e 2 co - occur , and s 2 is the set of records in which e 2 and e 3 co - occur . a bipartite graph g b =( v 1 ∪ v 2 , e ) is formed where v 1 is the two sets of records and v 2 is the all records in these sets . enumerating all maximum matchings in this graph yields all higher - order paths in the path group . the fourth diagram ( depicted in drawing 7 ) shows an example of one of the many paths in this path group . in this higher - order path , edge labels r 1 and r 4 are records in s 1 and s 2 and the path corresponds to the orange - colored maximum matching in the bipartite graph . in lieu of enumerating all maximum matchings to find the sdrs , in prior work [ 16 ] we also developed the following closed - forms for enumerating second - and third - order paths in path groups based on the inclusion - exclusion [ 24 ] principle : n − 4 =| a || b || c || d |−(| a ∩ b || c || d |+| a ∩ c || b || d |+| a ∩ d || b || c |+| b ∩ c || a || d |+| b ∩ d || a || c |+| c ∩ d || a || b |)+(| a ∩ b || c ∩ d |+| a ∩ c || b ∩ d |+| a ∩ d || b ∩ c |)+ 2 (| a ∩ b ∩ c || d |+| a ∩ b ∩ d || c |+| a ∩ c ∩ d || b |+| b ∩ c ∩ d || a |)− 6 (| a ∩ b ∩ c ∩ d |) ( 12 ) here the letters a , b and c represent sets of records for each edge in a third - order path group ( e . g ., e 1 -{ 2 , 3 , 4 }- e 2 -{ 1 , 3 , 5 }- e 3 -{ 2 , 3 , 5 }- e 4 , a ={ 2 , 3 , 4 }, b ={ 1 , 3 , 5 }, c ={ 2 , 3 , 5 }). although our application is not completely analogous to the enumeration of sets using the inclusion - exclusion principle , we were able to successfully develop these three closed - forms using inclusion - exclusion as a starting point . for orders higher than four we rely on our iterative approach to enumeration using bipartite graphs . this approach , known as “ higher - order naïve bayes ”, has been shown to significantly improve the classification performance when the training set is small . therefore this algorithm is ideal for producing an accurate topic - indexing system while keeping the amount of manual classification required by the user to a minimum . the output of the categorization algorithm is an index that is partitioned into subsections called ‘ topic indexes ’, with one topic index containing all entries that correspond to a particular topic . as data is added to each topic index , a ‘ lift ’ metric is computed to measure the density and quality of data in the topic index . the data in the topic indexes facilitate the advanced searching and recommendation features of the system . peer - to - peer ( p2p ) technology refers to a system that manages distributed resources to perform data sharing functions in a decentralized manner . the invention contains a customized software implementation of a peer - to - peer protocol that serves to automatically share the contents of the hard disk on a network to which other instances of the invention ( or compatible devices ) are connected . the peer - to - peer software is also customized to share the index data generated by the indexing software component . through the peer - to - peer software component , the invention acts as one node , or peer , on a large network of compatible devices . the peer - to - peer component works by first registering the device on the network with a unique generated id number . it contacts a server peer [ 28 ] and registers its id with a username and password supplied by the user of the system . if the authentication process succeeds , the client will be able to continue by requesting the ids of peers that are in the same group . the peer - to - peer software performs authentication and access control by means of user groups . the groups in the peer - to - peer network form a hierarchical structure . at the lowest level , a group is created for each individual user who is registered in the system . this provides each user full search , read , and write access for his or her own data from any remote location . at the next level there are “ user groups ” which are created by users and can be joined by an arbitrary number of other users . such groups may have open membership ( anyone who desires may join ), or invitation - only , by means of a digital certificate . a single user may be a member of any number of such groups . by default , these groups offer read - only data access to the members of the group . at the highest level there is the “ world ” group , consisting of all users of the system on any reachable network . the underlying mechanics of the peer - to - peer protocol perform discovery and routing functions that allow any type of data to be distributed between peers on the network without recourse to a centralized server . the peer - to - peer software incorporates advanced techniques for providing access to networked resources which are located behind firewalls or network address translation ( nat ) routers . the peer - to - peer software contains additional capabilities allowing the device to act as a “ server peer ” on the network . this added functionality is related to data transport , discovery and routing on the peer - to - peer network . server peers increase the reliability flexibility , and self - healing properties of the peer - to - peer network . the server peer functionality may be optionally activated by the end user . this component comprises an implementation of the lower - level set of networking protocols that allow the messages generated by the p2p protocol to be assembled into packets and transmitted across a computer network . in the preferred embodiment , the network protocols are embodied in hardware on an asic or set of asics . the hard disk is formatted so as to provide a separate partition for the index data . the indexing software runs continuously , monitoring the data that is written to the hard disk , and updating the topic indexes on disk dynamically . in the preferred embodiment , the indexing software runs on the coprocessor inside the invention &# 39 ; s housing , for as long as the system is powered on . in this way , the indexing software works transparently in conjunction with the normal file system operations , providing a more sophisticated form of data access in real time . the union of a transaction - based file system with automatic indexing software represents a significant advance in the ease of use and performance of data sharing systems . the security model in the peer - to - peer software is closely coupled to the acl security model of the file system . the single - user group in the p2p software corresponds to an individual user name in the file system , the user groups correspond to file system groups , and the world group corresponds to the world access classification in the file system . this improves security by eliminating “ gaps ” and “ leaky abstractions ” in the authentication and access model . the union of group - based authentication in the p2p software module with group - based acl &# 39 ; s in the file system overcomes deficiencies in the prior art related to the cohesiveness , complexity , and security of data sharing . the peer - to - peer software component is integrated with the indexing component in a novel way . the most significant feature of the integration of the peer - to - peer software with indexing is index sharing . indexes containing data classified under a single topic ( topic indexes ) are shared transparently on the network , according to the demands of end - user applications and the permissions that users specify for their topic indexes . the permissions of each topic index can be set independently of the data itself and independently of other topic indexes . the user of the system can select which of their topic indexes are shared with which groups . in this way the user chooses by topic which subsets of their data are shared , rather than by file location as is typical in file - sharing applications . furthermore , the peer - to - peer software is designed to treat index data that is transferred over the network differently from other types of data . each of the “ packets ” ( transmission units ) of data transferred by the peer - to - peer protocol contain a predefined set of signature bytes that identify them as either index data or regular data . if a peer node that is used to transfer data between two other hosts sees index data in the packets it is transferring , and it has permission to read the index , it will add that index data to its own index even though the node is not the original destination for the index data . in this way the distributed index of the p2p group &# 39 ; s data becomes more complete as the network is utilized . the sharing of topic indexes provides an efficient peer search method , in which all resources shared by a group can be searched . when the user performs a search of a group to which he or she has search access , the indexes of that group are downloaded to the user transparently through the peer - to - peer network . this is much more efficient and robust than attempting to discover and search all shared data on the network , or than maintaining a centralized index . through this the system automatically provides searching and recommendation of the shared data on the basis of content topics , which is a more advanced and intuitive method of sharing data . searching is just one sample application that is provided by means of topic index sharing ; others are detailed in [ 29 ], [ 30 ]. the peer - to - peer module forwards its messages to the network protocol module , enabling the network module to send its peer - to - peer messages to remote peer - to - peer modules transparently . the network module also decodes messages received from remote peer - to - peer modules and forwards them to the peer - to - peer module . the graphical user interface software is the primary means of operating the invention . upon installation of the device , the user interface allows the user to register their device with a specific username , join various pre - existing groups , select which topic indexes are shared with which groups , to browse the files on their own devices remotely , and to search by topic or metadata on any peers to which the user has access . this describes a minimal set of the functionality that is provided by any user interface to the invention . the invention facilitates the provision of a large number of user services , some of which are further described in patent applications [ 28 ] and [ 30 ]. the advanced data sharing capabilities of the invention allow for an unlimited variety of innovative services to be provided by various user interfaces and network configurations . in the preferred embodiment the invention also contains a firmware ip stack and http server . this allows a web - based graphical user interface , as described above , to be provided to any client device on the network to which the invention is connected . this allows a high level of the invention &# 39 ; s functionality to be accessed with no software installation necessary on a client device . this should be considered an optional component and not key to the originality or usefulness of the invention . the system also contains software which performs automated backups of data shared in the p2p network to which it is attached . the backup functionality is a use case of the p2p network &# 39 ; s provided functionality , and so the backup software should be considered to reside at the application level . any indexed shared data can be tagged for backup . a user can set backup tags for his or her data by folder , by index topic , or by individual file . this tag becomes part of the index of the user &# 39 ; s data . when the backup software is run , it retrieves all indexes in the network group and searches the index for all files tagged for backup . all tagged files are downloaded from the network and stored in a special backup archive on the hard drive . the backup archive preserves the device id and the directory location of each backup file so that a complete restoration can be performed . in this embodiment , there is no specialized hardware at all . the indexing , p2p , and user interface software modules all run on a host device such as a pc . 2 . only indexing is in hardware , and p2p and the user interface run on a host device . this embodiment has fewer components of the system in hardware or firmware than the preferred embodiment ; only the indexing module runs on the embedded platform . this provides many of the performance advantages of the preferred embodiment , as the indexing is the software function that is most closely wedded to the file system and storage hardware . in this embodiment , a specialized dedicated asic is used to embody all the software functions of the system on a single chip . while the above descriptions contain many specificities , these should not be construed as limitations on the scope of the invention , but rather as an exemplification of one preferred embodiment thereof . many other variations are possible , and in particular the scope of the invention is not limited by , for example , the type of data storage technology used , the size , configuration or materials of any casings or enclosures , or the hardware platform or operating system on which the software is run . accordingly , the scope of the invention should be determined not by the embodiments illustrated , but by the appended claims and their legal equivalents . in the preferred embodiment , the device is first plugged into a power outlet , it is connected to a network by an ethernet cable , and the power is switched on . at this point the device automatically acquires an ip address using dhcp . then the user uses a personal computer with a web browser to visit the web page provided by the device &# 39 ; s web server . using the web page interface , the user creates a username and password and specifies sharing permissions for the device . at a later date , the user can visit the web page interface , log in using the previously created username and password , and examine a summary of the index data created by the device , and alter the sharing permissions for a given topic . drawing 6 contains a flowchart of the indexing process , by which data to be written to the disk is analyzed and indexed . this is an automatic operation . b . taskar , p . abbeel , d . koller , discriminative probabilistic models for relational data , in proceedings of uncertainty in artificial intelligence conference uai02 , edmonton , canada , 2002 l . getoor and c . p . diehl , link mining : a survey . sigkdd explorations , 7 ( 2 ), 2005 , 3 - 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