Abstract:
Systems and methods to perform efficient searching for web content using a search engine are provided. In an illustrative implementation, a computing environment comprises a search engine computing application having an essential pages module operative to execute one or more selected selection algorithms to select content from a cooperating data store. In an illustrative operation, the exemplary search engine executes on a received search query to generate search results. Operatively, the retrieved results can be generated based upon their joint coverage of the submitted search query by deploying a selected sequential forward floating selection (SFFS) algorithm executing on the essential pages module. In the illustrative operation, the SFFS algorithm can operate to iteratively add one and delete one element from the set to improve a coverage score until no further improvement can be attained. The resultant processed search results can be considered essential pages.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of priority under 35 U.S.C Section 119 from U.S. Provisional Patent Application Ser. No. 61/015,735 entitled “ESSENTIAL PAGES”, filed on Dec. 21, 2007, the entirety of which is herein incorporated by reference. 
    
    
     BACKGROUND 
     The Internet contains a vast amount of information, distributed over a multitude of computers connected by “The Net”, hence providing users with large amounts of information on any topic imaginable. Although large amounts of information are available, however, finding the desired information is not always easy or fast. 
     Search engines have been developed to address the problem of finding desired information on the Internet. Typically a user who has an idea of the type of information desired, enters a search term or search terms and a search engine returns a list of web pages that contain the term or terms. Alternately, a user may want to browse through data, as for example, when a user is not sure what information is wanted. 
     Not surprisingly, web-search is one of the premium applications on the Internet, resulting in substantial advertisement revenues. Results to Web-search queries are typically influenced by several metrics: 1) {C}—content relevance derived from documents&#39; anchor text, title and headings, word frequency and proximity, file, directory, and domain names, and other more sophisticated forms of content analysis; 2) {U}—user behavior extrapolated from user&#39;s spent time-on-page, time-on-domain, click-through rates, etc.; 3) {P}—popularity in the global link structure with authority, readability, and novelty typically determining the linkage. 
     With current practices, links to the most “relevant,” according to the above criteria, pages are then potentially clustered and delivered to users who in turn browse the results to find the desired information. Although researched in detail along most of the mentioned criteria, search engines still leave a lot to be desired. With current practices there exists an important inefficiency of state-of-the-art search engines: content redundancy. Specifically, in queries where learning about a subject is objective, currently deployed search engines return unsatisfactory results as they consider the query coverage by each page individually, not a set of pages as a whole. 
     From the foregoing it is appreciated that there exists a need for systems and methods to ameliorate the shortcomings of existing practices. 
     SUMMARY 
     This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter. 
     The subject matter described herein allows for systems and methods to perform efficient searching for web content using a search engine. In an illustrative implementation, a computing environment comprises a search engine computing application having an essential pages module operative to execute one or more selected selection algorithms to select content from a cooperating data store. 
     In an illustrative operation, the exemplary search engine executes on a received search query to generate search results. Operatively, the retrieved results can be generated based upon their joint coverage of the submitted search query by deploying a selected sequential forward floating selection (SFFS) algorithm illustratively executing on the essential pages module. In the illustrative operation, the SFFS algorithm can operate to iteratively add one and delete one element from the set to improve a coverage score until no further improvement can be attained. The resultant processed search results can be considered essential pages. 
     In an illustrative operation, the resultant essential pages can be electronically delivered responsive to the received search query by the essential pages module to one or more cooperating computing environments. 
     The following description and the annexed drawings set forth in detail certain illustrative aspects of the subject matter. These aspects are indicative, however, of but a few of the various ways in which the subject matter can be employed and the claimed subject matter is intended to include all such aspects and their equivalents. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram of an exemplary computing environment executing a search engine. 
         FIG. 2  is a block diagram of an exemplary computing environment executing a search engine operative to identify essential pages. 
         FIG. 3  is a block diagram describing query coverage by a currently deployed search engine and a search engine using an essential pages search heuristic. 
         FIG. 4  is a graph describing the relationship of a word-importance score as a function of the word-relevance score. 
         FIG. 5  is an exemplary flow diagram showing illustrative processing performed when document indexing. 
         FIG. 6  is an exemplary flow diagram showing illustrative processing when calculating an exemplary global term frequency table. 
         FIG. 7  is a block diagram of an exemplary equation for use in determining essential pages. 
         FIG. 8  is a flow diagram of exemplary pseudo-code for an exemplary illustrative sequential forward floating algorithm used in identifying essential pages. 
         FIG. 9  is an example computing environment in accordance with various aspects described herein. 
         FIG. 10  is an example networked computing environment in accordance with various aspects described herein. 
     
    
    
     DETAILED DESCRIPTION 
     The claimed subject matter is now described with reference to the drawings, wherein like reference numerals are used to refer to like elements throughout. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the claimed subject matter. It may be evident, however, that the claimed subject matter may be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form in order to facilitate describing the claimed subject matter. 
     As used in this application, the word “exemplary” is used herein to mean serving as an example, instance, or illustration. Any aspect or design described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other aspects or designs. Rather, use of the word exemplary is intended to present concepts in a concrete fashion. 
     Additionally, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or”. That is, unless specified otherwise, or clear from context, “X employs A or B” is intended to mean any of the natural inclusive permutations. That is, if X employs A; X employs B; or X employs both A and B, then “X employs A or B” is satisfied under any of the foregoing instances. In addition, the articles “a” and “an” as used in this application and the appended claims should generally be construed to mean “one or more” unless specified otherwise or clear from context to be directed to a singular form. 
     Moreover, the terms “system,” “component,” “module,” “interface,” “model” or the like are generally intended to refer to a computer-related entity, either hardware, a combination of hardware and software, software, or software in execution. For example, a component may be, but is not limited to being, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and/or a computer. By way of illustration, both an application running on a controller and the controller can be a component. One or more components may reside within a process and/or thread of execution and a component may be localized on one computer and/or distributed between two or more computers. 
     Although the subject matter described herein may be described in the context of illustrative illustrations to process one or more computing application features/operations for a computing application having user-interactive components the subject matter is not limited to these particular embodiments. Rather, the techniques described herein can be applied to any suitable type of user-interactive component execution management methods, systems, platforms, and/or apparatus. 
     Search Engine Processing Overview: 
     Existing practices include a method to rank documents using an optimization framework to maximize the probability of finding a relevant document in a top set n. In addition to relevance, existing practices also consider diversity of Web-search results as an additional factor for ordering documents. A re-ranking technique based on maximum marginal relevance criterion to reduce redundancy from search results as well as presented document summarizations has been considered. Additionally, an affinity ranking scheme to re-rank search results by optimizing diversity and information richness of the topic and query results has been developed. Such practices model the variance of topics in groups of documents. 
     The herein described systems and methods provide a modeling of the overall knowledge space for a specific query and improving the coverage of this space by a set of documents. In an illustrative implementation a “bag-of-words” model for representing knowledge spaces is provided. Additionally, in the illustrative implementation, a formal notion of coverage over the “bag-of-words” is provided and a simple but systematic algorithm to select documents that maximize coverage is derived to allow relevance to the search topic. 
     Essential Pages: 
       FIG. 1  describes computing environment  100  operative to perform one or more web searches using an exemplary search engine. As is shown in  FIG. 1 , computing environment  100  comprises client computing environment  102 , communications network  110 , and server computing environment  112 . Further, as is shown in  FIG. 1 , client computing environment  102  further comprises computing application  104  which illustratively includes computing application processing area  106  and computing application display/interface area  108 . Further, as is shown in  FIG. 1 , server computing environment comprises search engine computing application  114  and cooperates with search engine data store  116 . 
     In an illustrative operation, a search request (e.g., providing a search query) can be communicated from client computing environment  102  (e.g., by receiving one or more inputs from a participating user (not shown) using computing application  104  computing application display area  108 ) over communications network  110  to server computing environment  112 . Responsive to the search request, server computing environment  112  executing search engine computing application  114  can process the search request and generate search results using data from search engine data store  116  according to one or more selected search heuristics. The generated search results can then be communication from server computing environment  112  to client computing environment  102  over communications network  110  for processing and display by computing application  104  (e.g., a web browser computing application) utilizing computing application processing area  106  and computing application display/interface area  108 . 
       FIG. 2  describes computing environment  200  operative to perform one or more web searches using an exemplary search engine. As is shown in  FIG. 2 , computing environment  200  comprises client computing environment  202 , communications network  210 , and server computing environment  212 . Further, as is shown in  FIG. 2 , client computing environment  202  further comprises computing application  204  which illustratively includes computing application processing area  206  and computing application display/interface area  208 . Further, as is shown in  FIG. 2 , server computing environment comprises search engine computing application  214  having essential pages module  218  and cooperates with search engine data store  216 . 
     In an illustrative operation, a search request (e.g., providing a search query) can be communicated from client computing environment  202  (e.g., by receiving one or more inputs from a participating user (not shown) using computing application  204  computing application display area  208 ) over communications network  210  to server computing environment  212 . Responsive to the search request, server computing environment  212  executing search engine computing application  214  can process the search request and generate search results using data from search engine data store  216  according to one or more selected search heuristics provided by essential pages module  218 . The generated search results can then be communication from server computing environment  212  to client computing environment  202  over communications network  210  for processing and display by computing application  204  (e.g., a web browser computing application) utilizing computing application processing area  206  and computing application display/interface area  208 . 
     It is appreciated that although, essential pages module  218  is depicted to operatively run on server computing environment  212  that such depiction is merely illustrative as the herein described systems and methods can be illustratively deployed such that the essential pages module is operative on, in whole or in part, on client computing environment  202 . 
       FIG. 3  schematically illustrates an illustrative implementation of how S Q  (i.e., the total knowledge that exists on the Web about a given query Q) is covered by a set of pages computed using a traditional page ranking algorithm as described by graph  302  and a set of essential pages assembled to maximize their joint query coverage as described by graph  304 . As shown by graph  302 , in the traditional model, in order to learn details about S Q , users have to browse substantially more pages (e.g., as denoted by the size of S Q ). In the illustrative implementation, a traditional search engine can be used to obtain a list of relevant URLs for a given query and then subsequently processed according to a selected essential pages re-ordering algorithm as part of an illustrative post processing exemplary method as described by graph  304 . 
       FIG. 4  graphically illustrates, via graph  400 , the relationship of word-importance score  402  as function of a word-relevance score  404 . In an illustrative implementation, a search engine is provides that illustratively operates to find a set of pages that gives maximum coverage about a particular search query Q over an exemplary related knowledge space S Q . In the illustrative implementation, a relevance based rand ordered search engine deploying a “bag-of-words” approach is provided to execute on the essential pages search heuristic. With a “bag-of-words” approach, a document is processed as a collection of statistics over a set (i.e., bag, of words used in it, without explicit semantic constructions such as sentences, formatting, etc.). In an illustrative operation, a web-page can be considered as a bag-of-words where each distinct word is associated with the total number of times the word appears in a specific document. 
       FIG. 5  describes an illustrative method  500  for performing document indexing for essential pages. As is shown, processing begins at block  505  where for each document j in an exemplary database D the words in the document are first extracted. From there, processing proceeds to block  510  where the exemplary database of documents is classified so that the m-th word in the j-th document can be described. Processing then proceeds to block  515  were word stemming is performed. In an illustrative operation, the word root is retained while word endings are removed. Illustratively, words such as “as,” “is,” “be,” etc., in a pre-defined set of stop-words can be then removed as they do not describe the context semantics. Illustratively, stemming and stopping as performed at block  520  can improve search performance by giving users more pertinent results; they also reduce the search complexity by reducing the dictionary of words. Illustratively, the total number of unique terms in the resulting list T as Nt can be denoted. Term frequency T F (i, j) can indicate the number of times i-th term appears in j-th document. The term frequency information for D and T can be illustratively organized as a term frequency table of size Nt×Nd. To facilitate fast access, a hash table can be constructed at block  525  to map each term to the corresponding row of the term frequency table as performed at block  535 . Additionally, as is shown in  FIG. 5 , updates to the bag-of-words and term frequency table can be performed at block  530 . 
       FIG. 6  describes illustrative method  600  for computing and storing an exemplary global term-frequency table. As is shown, processing begins at block  605  where a query is received. Processing then proceeds to block  610  where a query word Q first undergoes stemming and then stopping at block  615 . From there processing proceeds to block  620  where the hash value of the query word can be used to point at block  625  to the corresponding row of the term frequency table as described in block  630 . 
     In context to the processing described in  FIGS. 5 and 6 , given a single-term query Q, the subset of documents, DQ, containing Q is identified using the global term-frequency table. As DQ contains all the information about Q, the set of terms (bag-of-words) extracted from DQ as SQ can illustratively denoted. Essential page selection can be described a subset of documents EQ ½ DQ that provides maximum coverage about the query. In an illustrative implementation, N d   Q =∥D Q ∥ and N t   Q =∥S Q | are set. In the illustrative implementation, the documents in DQ contain the query term. For queries containing multiple terms, {Q(1), . . . , W(m)} at least one of these terms appears in each document in DQ. The subset of the global term-frequency table that relates to the search query Q as TF Q =S Q ×D Q  can be illustratively denoted and its size N t   Q ×N d   Q  can be recorded. For each word, w εS Q , relevant to the query, a query-relevance score can be defined r(w) can be expressed: 
                 r   ⁡     (   w   )       =       n   w   Q       N   d   Q         ,         
where n w   Q  represents the number of documents in DQ which contain w. The query-relevance score measures how relevant w is to Q; the higher the score, the higher the relevance.
 
     In the illustrative implementation, a coverage score can be defined as C(j) of a document jεD Q  to be: 
     
       
         
           
             
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     where TF Q (i, j) represents the term-frequency value of the i-th word, ω i  in document j. The term γ(ω i ) can be used to quantify the overall importance given to covering ω i  in E Q  which can be considered the word-importance score, which can be defined as: 
               γ   ⁡     (     w   i     )       =       r   ⁡     (     w   i     )       ⁢         log   2     ⁡     [     1     r   ⁡     (     w   i     )         ]       .               FIG. 4  shows the variation of γ(ω i ) vs. r(ω i ). As is shown in  FIG. 4  the word importance metric can be described by the following illustrative descriptions: 1) Low r(wi)—words that are less relevant to the query can be considered to not provide significant information about the query, and can be considered less important; 2) High r(wi)—words that are very relevant to the query (such as the query words itself) can be considered to provide more information about the query; 3) Important words—for words that lie in between the above two cases, word-importance can be considered relatively high and our algorithms aim at covering these words with as few as possible pages from DQ. In the illustrative implementation, generalizing on the word-importance score equation, a joint coverage score of set of documents can be defined by the equation:
 
     
       
         
           
             
               
                 
                   
                     
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     Where two documents a and b having corresponding bag-of-words S Q   a  and S Q   b . 
       FIG. 7  describes the essential pages equation  700  which illustratively operates to maximize the joint coverage score (as described by  FIG. 4 ). As is shown in  FIG. 7 , the essential pages equation can be described by: 
     
       
         
           
             
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       FIG. 8  illustratively presents pseudo-code  800  for the execution of an exemplary SFFS algorithm in identifying essential pages. As is shown in  FIG. 8 , E=0 is set at step 1. Iteratively, one element is added and deleted from the set to improve coverage. In the adding step shown in step 3, a document k⊂D Q  which when added to set E can result in maximum coverage. Document k is then added to E in step 5 if the condition C(E∪k)&gt;C(E) is met. In the deletion step 6, a document m which adds the least amount of information to the knowledge space covered by E, can be removed from E in step 8 conditional on C(E−m)&gt;C(E). This iterative process, as is described in  FIG. 6 , can be repeated until no further improvement can be attained or ∥E∥=n Q . 
     The methods can be implemented by computer-executable instructions stored on one or more computer-readable media or conveyed by a signal of any suitable type. The methods can be implemented at least in part manually. The steps of the methods can be implemented by software or combinations of software and hardware and in any of the ways described above. The computer-executable instructions can be the same process executing on a single or a plurality of microprocessors or multiple processes executing on a single or a plurality of microprocessors. The methods can be repeated any number of times as needed and the steps of the methods can be performed in any suitable order. 
     The subject matter described herein can operate in the general context of computer-executable instructions, such as program modules, executed by one or more components. Generally, program modules include routines, programs, objects, data structures, etc., that perform particular tasks or implement particular abstract data types. Typically, the functionality of the program modules can be combined or distributed as desired. Although the description above relates generally to computer-executable instructions of a computer program that runs on a computer and/or computers, the user interfaces, methods and systems also can be implemented in combination with other program modules. Generally, program modules include routines, programs, components, data structures, etc. that perform particular tasks and/or implement particular abstract data types. 
     Moreover, the subject matter described herein can be practiced with most any suitable computer system configurations, including single-processor or multiprocessor computer systems, mini-computing devices, mainframe computers, personal computers, stand-alone computers, hand-held computing devices, wearable computing devices, microprocessor-based or programmable consumer electronics, and the like as well as distributed computing environments in which tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules can be located in both local and remote memory storage devices. The methods and systems described herein can be embodied on a computer-readable medium having computer-executable instructions as well as signals (e.g., electronic signals) manufactured to transmit such information, for instance, on a network. 
     Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing some of the claims. 
     It is, of course, not possible to describe every conceivable combination of components or methodologies that fall within the claimed subject matter, and many further combinations and permutations of the subject matter are possible. While a particular feature may have been disclosed with respect to only one of several implementations, such feature can be combined with one or more other features of the other implementations of the subject matter as may be desired and advantageous for any given or particular application. 
     Moreover, it is to be appreciated that various aspects as described herein can be implemented on portable computing devices (e.g., field medical device), and other aspects can be implemented across distributed computing platforms (e.g., remote medicine, or research applications). Likewise, various aspects as described herein can be implemented as a set of services (e.g., modeling, predicting, analytics, etc.). 
       FIG. 9  illustrates a block diagram of a computer operable to execute the disclosed architecture. In order to provide additional context for various aspects of the subject specification,  FIG. 9  and the following discussion are intended to provide a brief, general description of a suitable computing environment  900  in which the various aspects of the specification can be implemented. While the specification has been described above in the general context of computer-executable instructions that may run on one or more computers, those skilled in the art will recognize that the specification also can be implemented in combination with other program modules and/or as a combination of hardware and software. 
     Generally, program modules include routines, programs, components, data structures, etc., that perform particular tasks or implement particular abstract data types. Moreover, those skilled in the art will appreciate that the inventive methods can be practiced with other computer system configurations, including single-processor or multiprocessor computer systems, minicomputers, mainframe computers, as well as personal computers, hand-held computing devices, microprocessor-based or programmable consumer electronics, and the like, each of which can be operatively coupled to one or more associated devices. 
     The illustrated aspects of the specification may also be practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules can be located in both local and remote memory storage devices. 
     A computer typically includes a variety of computer-readable media. Computer-readable media can be any available media that can be accessed by the computer and includes both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, computer-readable media can comprise computer storage media and communication media. Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by the computer. 
     Communication media typically embodies computer-readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism, and includes any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. Combinations of the any of the above should also be included within the scope of computer-readable media. 
     More particularly, and referring to  FIG. 9 , an example environment  900  for implementing various aspects as described in the specification includes a computer  902 , the computer  902  including a processing unit  904 , a system memory  906  and a system bus  908 . The system bus  908  couples system components including, but not limited to, the system memory  906  to the processing unit  904 . The processing unit  904  can be any of various commercially available processors. Dual microprocessors and other multi-processor architectures may also be employed as the processing unit  904 . 
     The system bus  908  can be any of several types of bus structure that may further interconnect to a memory bus (with or without a memory controller), a peripheral bus, and a local bus using any of a variety of commercially available bus architectures. The system memory  906  includes read-only memory (ROM)  910  and random access memory (RAM)  912 . A basic input/output system (BIOS) is stored in a non-volatile memory  910  such as ROM, EPROM, EEPROM, which BIOS contains the basic routines that help to transfer information between elements within the computer  902 , such as during start-up. The RAM  912  can also include a high-speed RAM such as static RAM for caching data. 
     The computer  902  further includes an internal hard disk drive (HDD)  914  (e.g., EIDE, SATA), which internal hard disk drive  914  may also be configured for external use in a suitable chassis (not shown), a magnetic floppy disk drive (FDD)  916 , (e.g., to read from or write to a removable diskette  918 ) and an optical disk drive  920 , (e.g., reading a CD-ROM disk  922  or, to read from or write to other high capacity optical media such as the DVD). The hard disk drive  914 , magnetic disk drive  916  and optical disk drive  920  can be connected to the system bus  908  by a hard disk drive interface  924 , a magnetic disk drive interface  926  and an optical drive interface  928 , respectively. The interface  924  for external drive implementations includes at least one or both of Universal Serial Bus (USB) and IEEE 1394 interface technologies. Other external drive connection technologies are within contemplation of the subject specification. 
     The drives and their associated computer-readable media provide nonvolatile storage of data, data structures, computer-executable instructions, and so forth. For the computer  902 , the drives and media accommodate the storage of any data in a suitable digital format. Although the description of computer-readable media above refers to a HDD, a removable magnetic diskette, and a removable optical media such as a CD or DVD, it should be appreciated by those skilled in the art that other types of media which are readable by a computer, such as zip drives, magnetic cassettes, flash memory cards, cartridges, and the like, may also be used in the example operating environment, and further, that any such media may contain computer-executable instructions for performing the methods of the specification. 
     A number of program modules can be stored in the drives and RAM  912 , including an operating system  930 , one or more application programs  932 , other program modules  934  and program data  936 . All or portions of the operating system, applications, modules, and/or data can also be cached in the RAM  912 . It is appreciated that the specification can be implemented with various commercially available operating systems or combinations of operating systems. 
     A user can enter commands and information into the computer  902  through one or more wired/wireless input devices, e.g., a keyboard  938  and a pointing device, such as a mouse  940 . Other input devices (not shown) may include a microphone, an IR remote control, a joystick, a game pad, a stylus pen, touch screen, or the like. These and other input devices are often connected to the processing unit  904  through an input device interface  942  that is coupled to the system bus  908 , but can be connected by other interfaces, such as a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, etc. 
     A monitor  944  or other type of display device is also connected to the system bus  908  via an interface, such as a video adapter  946 . In addition to the monitor  944 , a computer typically includes other peripheral output devices (not shown), such as speakers, printers, etc. 
     The computer  902  may operate in a networked environment using logical connections via wired and/or wireless communications to one or more remote computers, such as a remote computer(s)  948 . The remote computer(s)  948  can be a workstation, a server computer, a router, a personal computer, portable computer, microprocessor-based entertainment appliance, a peer device or other common network node, and typically includes many or all of the elements described relative to the computer  902 , although, for purposes of brevity, only a memory/storage device  950  is illustrated. The logical connections depicted include wired/wireless connectivity to a local area network (LAN)  952  and/or larger networks, e.g., a wide area network (WAN)  954 . Such LAN and WAN networking environments are commonplace in offices and companies, and facilitate enterprise-wide computer networks, such as intranets, all of which may connect to a global communications network, e.g., the Internet. 
     When used in a LAN networking environment, the computer  902  is connected to the local network  952  through a wired and/or wireless communication network interface or adapter  956 . The adapter  956  may facilitate wired or wireless communication to the LAN  952 , which may also include a wireless access point disposed thereon for communicating with the wireless adapter  956 . 
     When used in a WAN networking environment, the computer  902  can include a modem  958 , or is connected to a communications server on the WAN  954 , or has other means for establishing communications over the WAN  954 , such as by way of the Internet. The modem  958 , which can be internal or external and a wired or wireless device, is connected to the system bus  908  via the serial port interface  942 . In a networked environment, program modules depicted relative to the computer  902 , or portions thereof, can be stored in the remote memory/storage device  950 . It will be appreciated that the network connections shown are example and other means of establishing a communications link between the computers can be used. 
     The computer  902  is operable to communicate with any wireless devices or entities operatively disposed in wireless communication, e.g., a printer, scanner, desktop and/or portable computer, portable data assistant, communications satellite, any piece of equipment or location associated with a wirelessly detectable tag (e.g., a kiosk, news stand, restroom), and telephone. This includes at least Wi-Fi and Bluetooth™ wireless technologies. Thus, the communication can be a predefined structure as with a conventional network or simply an ad hoc communication between at least two devices. 
     Wi-Fi, or Wireless Fidelity, allows connection to the Internet from a couch at home, a bed in a hotel room, or a conference room at work, without wires. Wi-Fi is a wireless technology similar to that used in a cell phone that enables such devices, e.g., computers, to send and receive data indoors and out; anywhere within the range of a base station. Wi-Fi networks use radio technologies called IEEE 802.11(a, b, g, etc.) to provide secure, reliable, fast wireless connectivity. A Wi-Fi network can be used to connect computers to each other, to the Internet, and to wired networks (which use IEEE 802.3 or Ethernet). Wi-Fi networks operate in the unlicensed 2.4 and 5 GHz radio bands, at an 11 Mbps (802.11a) or 54 Mbps (802.11b) data rate, for example, or with products that contain both bands (dual band), so the networks can provide real-world performance similar to the basic 10BaseT wired Ethernet networks used in many offices. 
     Referring now to  FIG. 10 , there is illustrated a schematic block diagram of an exemplary computing environment  1000  in accordance with the subject invention. The system  1000  includes one or more client(s)  1010 . The client(s)  1010  can be hardware and/or software (e.g., threads, processes, computing devices). The client(s)  1010  can house cookie(s) and/or associated contextual information by employing the subject invention, for example. The system  1000  also includes one or more server(s)  1020 . The server(s)  1020  can also be hardware and/or software (e.g., threads, processes, computing devices). The servers  1020  can house threads to perform transformations by employing the subject methods and/or systems for example. One possible communication between a client  1010  and a server  1020  can be in the form of a data packet adapted to be transmitted between two or more computer processes. The data packet may include a cookie and/or associated contextual information, for example. The system  1000  includes a communication framework  1030  (e.g., a global communication network such as the Internet) that can be employed to facilitate communications between the client(s)  1010  and the server(s)  1020 . 
     Communications can be facilitated via a wired (including optical fiber) and/or wireless technology. The client(s)  1010  are operatively connected to one or more client data store(s)  1040  that can be employed to store information local to the client(s)  1010  (e.g., cookie(s) and/or associated contextual information). Similarly, the server(s)  1020  are operatively connected to one or more server data store(s)  1050  that can be employed to store information local to the servers  1020 . 
     What has been described above includes examples of the claimed subject matter. It is, of course, not possible to describe every conceivable combination of components or methodologies for purposes of describing the claimed subject matter, but one of ordinary skill in the art may recognize that many further combinations and permutations of the claimed subject matter are possible. Accordingly, the claimed subject matter is intended to embrace all such alterations, modifications and variations that fall within the spirit and scope of the appended claims. Furthermore, to the extent that the term “includes” is used in either the detailed description or the claims, such term is intended to be inclusive in a manner similar to the term “comprising” as “comprising” is interpreted when employed as a transitional word in a claim.