Patent Publication Number: US-9836544-B2

Title: Methods and systems for prioritizing a crawl

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is the continuation of U.S. patent application Ser. No. 13/752,292, filed Jan. 28, 2013 (now allowed), which is a continuation of U.S. patent application Ser. No. 10/940,314, filed Sep. 14, 2004 (now U.S. Pat. No. 8,386,728), which is a continuation-in-part of U.S. patent application Ser. No. 10/815,060, filed Mar. 31, 2004 (now abandoned). The disclosures of the above applications are expressly incorporated herein by reference to their entireties. 
    
    
     FIELD OF THE INVENTION 
     The invention generally relates to search engines. More particularly, the invention relates to methods and systems for prioritizing a crawl. 
     BACKGROUND OF THE INVENTION 
     Users generate and access a large number of articles, such as e-mails, web pages, word processing documents, spreadsheet documents, instant messenger messages, and presentation documents, using a client device, such as a personal computer, personal digital assistant, or mobile phone. Some articles are stored on one or more storage devices coupled to, accessible by, or otherwise associated with the client device(s). Users sometimes wish to search the storage device(s) for articles. 
     Conventional search applications “crawl” a storage device to index data stored on the storage device. For example, a conventional network-device search application crawls various storage devices associated with a network, such as the Internet, to locate data such as articles on the various storage devices. Links to articles and associated data are stored for subsequent retrieval by the network-device search application, such as in response to a search query. 
     Crawls performed by some conventional client-device search applications can significantly degrade the performance of the client device. For example, some conventional client-device search applications typically use batch processing to index some or all articles, which can result in noticeably slower performance of the client device during the batch processing. Additionally, batch processing occurs only periodically. Therefore, when a user performs a search, the most recent articles are sometimes not included in the results. Moreover, if the batch processing is scheduled for a time when the client device is not operational and is thus not performed for an extended period of time, the index of articles associated with the client device can become outdated. Some conventional client-device search applications also need to rebuild the index at each batch processing or build new partial indexes and perform a merge operation that can use a lot of client-device resources. Some conventional client-device search applications also sometimes use a great deal of memory when operational, resulting in slower performance of the client device. 
     Additionally, conventional search applications can require an explicit search query from a user to generate results. 
     SUMMARY OF THE INVENTION 
     Embodiments of the present invention comprise methods and systems that prioritize a crawl. One aspect of one embodiment of the present invention comprises identifying a plurality of storage locations each comprising a plurality of articles, ranking the plurality of storage locations based at least in part on events associated with the plurality of articles, and crawling the storage locations based at least in part on the ranking. 
     Another aspect of one embodiment of the present invention comprises identifying a plurality of storage locations each comprising a plurality of articles, identifying a plurality of types of the plurality of articles, ranking the plurality of storage locations based at least in part on the plurality of types of the plurality of articles, and crawling the storage locations based at least in part on the ranking. 
     Another aspect of one embodiment of the present invention comprises identifying a plurality of storage locations each comprising a plurality of articles, identifying a plurality of types of the plurality of articles, ranking the plurality of storage locations based at least in part on the plurality of types of the plurality of articles and based at least in part on events associated with the plurality of articles, and crawling the storage locations based at least in part on the ranking. 
     These exemplary embodiments are mentioned not to limit or define the invention, but to provide examples of embodiments of the invention to aid understanding thereof. Exemplary embodiments are discussed in the Detailed Description, and further description of the invention is provided there. Advantages offered by the various embodiments of the present invention may be further understood by examining this specification. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       These and other features, aspects, and advantages of the present invention are better understood when the following Detailed Description is read with reference to the accompanying drawings, wherein: 
         FIG. 1  illustrates a block diagram of a system in accordance with one embodiment of the present invention; 
         FIG. 2  illustrates a flow diagram of a method in accordance with one embodiment of the present invention; and 
         FIG. 3  illustrates a flow diagram of another method in accordance with one embodiment of the present invention. 
         FIG. 4  illustrates a flow diagram of another method in accordance with one embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION 
     Introduction 
     Embodiments of the present invention comprise methods and systems for prioritizing a crawl. There are multiple embodiments of the present invention. By way of introduction and example, in one exemplary embodiment of the present invention, a search engine can identify relevant content, such as documents, in particular storage locations on a hard drive or other storage devices associated with a client device. Depending on the relevancy of the content in each storage location, such as the presence of a frequently accessed document in a particular storage location, the storage locations can be prioritized for crawling. The search engine can then crawl the relevant content in these storage locations in a prioritized manner and index the content to make the content available for a subsequent search result. 
     By way of one example, a search engine can identify file directories associated with frequently accessed documents in a “My Favorites” folder on a hard drive. The search engine can rank each of these file directories according to the frequency of access associated with a predefined number of documents, such as ranking the file directories associated with the top 20 most frequently accessed documents in a “My Favorites” folder. In this manner, a frequently accessed document “patentl.doc” in directory “c:/ip” that has previously been accessed 100 times by a particular user can be more relevant than another frequently accessed document such as “patentl.xls” in directory “c:/fun” that has been previously accessed 9 times by the particular user. Thus, the file directories (such as “c:/ip”) associated with the more frequently accessed documents (such as “patentl.doc”) can be ranked relatively higher than the directories (such as “c:/fun”) associated with the less frequently accessed documents (such as “patentl.xls”). The search engine can then crawl the storage locations based at least in part on the ranking, such as crawling the file directory “c:/ip” containing the more frequently accessed document “patentl.doc” prior to crawling, if at all, file directory “c:/fun” containing the less frequently accessed document “patentl.xls.” In this manner, the search engine can initially crawl the file directories which contain the most frequently accessed documents on a user&#39;s hard drive. 
     This introduction is given to introduce the reader to the general subject matter of the application. By no means is the invention limited to such subject matter. Exemplary embodiments are described below. 
     System Architecture 
     Various systems in accordance with the present invention may be constructed.  FIG. 1  is a diagram illustrating an exemplary system in which exemplary embodiments of the present invention may operate. The present invention may operate, and be embodied in, other systems as well. 
     Referring now to the drawings in which like numerals indicate like elements throughout the several figures,  FIG. 1  is a block diagram illustrating an exemplary environment for implementation of an embodiment of the present invention. While the environment shown in  FIG. 1  reflects a client-side search engine program architecture embodiment, other embodiments are possible. The system  100  shown in  FIG. 1  includes multiple client devices  102   a - n  that can communicate with a server device  150  over a network  106 . The network  106  shown in  FIG. 1  comprises the Internet. In other embodiments, other networks, such as an Intranet, may be used instead. Moreover, methods according to the present invention may operate within a single client device that does not communicate with a server device or a network. 
     The client devices  102   a - n  shown in  FIG. 1  each include a computer readable medium  108 . The embodiment shown in  FIG. 1  includes a random access memory (RAM)  108  coupled to a processor  110 . The processor  110  executes computer executable program instructions stored in memory  108 . Such processors may include a microprocessor, an ASIC, state machines, or other processors, and can be any of a number of suitable computer processors, such as processors from Intel Corporation of Santa Clara, Calif. and Motorola Corporation of Schaumburg, Ill. Such processors include, or may be in communication with, media, for example computer-readable media, which stores instructions that, when executed by the processor, cause the processor to perform the steps described herein. Embodiments of computer-readable media include, but are not limited to, an electronic, optical, magnetic, or other storage or transmission device capable of providing a processor, such as the processor  110  of client  102   a , with computer-readable instructions. Other examples of suitable media include, but are not limited to, a floppy disk, CD-ROM, DVD, magnetic disk, memory chip, ROM, RAM, an ASIC, a configured processor, all optical media, all magnetic tape or other magnetic media, or any other medium from which a computer processor can read instructions. Also, various other forms of computer-readable media may transmit or carry instructions to a computer, including a router, private or public network, or other transmission device or channel, both wired and wireless. The instructions may comprise code from any suitable computer-programming language, including, for example, C, C++, C#, Visual Basic, Java, Python, Perl, and JavaScript. 
     Client devices  102   a - n  can be coupled to a network  106 , or alternatively, can be stand alone machines. Client devices  102   a - n  may also include a number of external or internal devices such as a mouse, a CD-ROM, DVD, a keyboard, a display device, or other input or output devices. Examples of client devices  102   a - n  are personal computers, digital assistants, personal digital assistants, cellular phones, mobile phones, smart phones, pagers, digital tablets, laptop computers, Internet appliances, and other processor-based devices. In general, the client devices  102   a - n  may be any type of processor-based platform that operates on any suitable operating system, such as Microsoft® Windows® or Linux, capable of supporting one or more client application programs. For example, the client device  102   a  shown can comprise a personal computer executing client application programs, also known as client applications  120 . The client applications  120  can be contained in memory  108  and can include, for example, a word processing application, a spreadsheet application, an e-mail application, an instant messenger application, a presentation application, an Internet browser application, a calendar/organizer application, a video playing application, an audio playing application, an image display application, a file management program, an operating system shell, and other applications capable of being executed by a client device. Client applications may also include client-side applications that interact with or accesses other applications (such as, for example, a web-browser executing on the client device  102   a  that interacts with a remote e-mail server to access c-mail). 
     The user  112   a  can interact with the various client applications  120  and articles associated with the client applications  120  via various input and output devices of the client device  102   a . Articles include, for example, word processor documents, spreadsheet documents, presentation documents, e-mails, instant messenger messages, database entries, calendar entries, appointment entries, task manager entries, source code files, and other client application program content, files, messages, items, web pages of various formats, such as HTML, XML, XHTML, Portable Document Format (PDF) files, and media files, such as image files, audio files, and video files, or any other documents or items or groups of documents or items or information of any suitable type whatsoever. 
     The user&#39;s  112   a  interaction with articles, the client applications  120 , and the client device  102   a  creates event data that may be observed, recorded, analyzed or otherwise used. An event can be any occurrence possible associated with an article, client application  120 , or client device  102   a , such as inputting text in an article, displaying an article on a display device, sending an article, receiving an article, manipulating an input device, opening an article, saving an article, printing an article, closing an article, opening a client application program, closing a client application program, idle time, processor load, disk access, memory usage, bringing a client application program to the foreground, changing visual display details of the application (such as resizing or minimizing) and any other suitable occurrence associated with an article, a client application program, or the client device whatsoever. Additionally, event data can be generated when the client device  102   a  interacts with an article independent of the user  112   a , such as when receiving an e-mail or performing a scheduled task. 
     The memory  108  of the client device  102   a  can also contain a capture processor  124 , a queue  126 , and a search engine  122 . The client device  102   a  can also contain or is in communication with a data store  140 . The capture processor  124  can capture events and pass them to the queue  126 . The queue  126  can pass the captured events to the search engine  122  or the search engine  122  can retrieve new events from the queue  126 . In one embodiment, the queue  126  notifies the search engine  122  when a new event arrives in the queue  126  and the search engine  122  retrieves the event (or events) from the queue  126  when the search engine  122  is ready to process the event (or events). When the search engine  122  receives an event it can be processed and can be stored in the data store  140 . The search engine  122  can receive an explicit query from the user  112   a  or generate an implicit query and it can retrieve information from the data store  140  in response to the query. In another embodiment, the queue is located in the search engine  122 . In still another embodiment, the client device  102   a  does not have a queue and the events are passed from the capture processor  124  directly to the search engine  122 . According to other embodiments, the event data is transferred using an information exchange protocol. The information exchange protocol can comprise, for example, any suitable rule or conversion facilitating data exchange, and can include, for example, any one of the following communication mechanisms: Extensible Markup Language—Remote Procedure Calling protocol (XML/RPC), Hypertext Transfer Protocol (HTTP), Simple Object Access Protocol (SOAP), shared memory, sockets, local or remote procedure calling, or any other suitable information exchange mechanism. 
     The capture processor  124  can capture an event by identifying and extracting event data associated with an event. Examples of events include sending or receiving an e-mail message, a user viewing a web page, saving a word processing document, printing a spreadsheet document, inputting text to compose or edit an e-mail, opening a presentation application, closing an instant messenger application, entering a keystroke, moving the mouse, and hovering the mouse over a hyperlink. An example of event data captured by the capture processor  124  for an event involving the viewing of a web page by a user can comprise the URL of the web page, the time and date the user viewed the web page, the content of the web page in original or processed forms, a screenshot of the page as displayed to the user, a thumbnail version of the screenshot, and image data associated with the web page. An example of event data captured by the capture processor  124  for an event involving receipt of an e-mail message by the user  112   a  can comprise the sender of the message, the recipients of the message, the time and date the message was received, the content of the message, and a conversation ID. A conversation ID can be used to associate messages that form a conversation and can be provided by the instant messenger application or can be generated by the capture processor  124 . A conversation can be one or more messages between the user  112   a  and at least one other user until the user  112   a  logs out of or closes the instant messenger application or the instant messenger application is inactive for a certain period of time (for example, 30 minutes). 
     In the embodiment shown, the capture processor  124  comprises multiple capture components. For example, the capture processor  124  shown in  FIG. 1  comprises a separate capture component for each client application in order to capture events associated with each application. The capture processor  124  shown also comprises a separate capture component that monitors overall network activity in order to capture event data associated with network activity, such as the receipt or sending of an instant messenger message. The capture processor  124  shown in  FIG. 1  can also comprise a separate client device capture component that monitors overall client device performance data, such as processor load, idle time, disk access, the client applications in use, and the amount of memory available. The capture processor  124  shown in  FIG. 1  also can comprise a separate capture component to monitor and capture keystrokes input by the user and a separate capture component to monitor and capture items, such as text, displayed on a display device associated with the client device  102 . An individual capture component can monitor multiple client applications and multiple capture components can monitor different aspects of a single client application. 
     In one embodiment, the capture processor  124 , through the individual capture components, can monitor activity on the client device and can capture events by a generalized event definition and registration mechanism, such as an event schema. Each capture component can define its own event schema or can use a predefined one. Event schema can differ depending on the client application or activity the capture component is monitoring. Generally, the event schema can describe the format for an event, for example, by providing fields for event data associated with the event (such as the time of the event) and fields related to any associated article (such as the title) as well as the content of any associated article (such as the document body). An event schema can describe the format for any suitable event data that relates to an event. For example, an event schema for an instant messenger event sent by a user  112   a  can include a recipient or list of recipients, the time sent, the date sent, content of the message, and a conversation ID. An event schema for a web page currently being viewed by a user can include the Uniform Resource Locator (URL) of the web page, the time being viewed, and the content of the web page. An event schema for a word processing document being saved by a user can include the title of the document, the time saved, the format of the document, the text of the document, and the location of the document. More generally, an event schema can describe the state of the system around the time of the event. For example, an event schema can contain a URL for a web page event associated with a previous web page that the user navigated from. An event schema can also contain lists of information, such as, for example, multiple recipient fields from an e-mail. An event schema can also contain optional fields so that an application can include additional event data if desired. 
     The capture processor  124  can capture events occurring presently (or “real-time events”) and can capture events that have occurred in the past (or “historical events”). Real-time events can be “indexable” or “non-indexable.” In one embodiment, the search engine  122  indexes indexable real-time events, but does not index non-indexable real-time events. The search engine  122  may determine whether to index an event based on the importance of the event. Indexable real-time events can be more important events associated with an article, such as viewing a web page, loading or saving a file, and receiving or sending an instant message or e-mail. Non-indexable events can be deemed not important enough by the search engine  122  to index and store the event, such as moving the mouse or selecting a portion of text in an article. Non-indexable events can be used by the search engine  122  to update the current user state. While all real-time events can relate to what the user is currently doing (or the current user state), indexable real-time events can be indexed and stored in the data store  140 . Alternatively, the search engine  122  can index all real-time events. Real-time events can include, for example, sending or receiving an article, such as an instant messenger message, examining a portion of an article, such as selecting a portion of text or moving a mouse over a portion of a web page, changing an article, such as typing a word in an e-mail or pasting a sentence in a word processing document, closing an article, such as closing an instant messenger window or changing an e-mail message being viewed, loading, saving, opening, or viewing an article, such as a word processing document, web page, or e-mail, listening to or saving an MP3 file or other audio/video file, or updating the metadata of an article, such as book marking a web page, printing a presentation document, deleting a word processing document, or moving a spreadsheet document. 
     Historical events are similar to indexable real-time events except that the event occurred before the installation of the search engine  122  or was otherwise not captured, because, for example, the search engine  122  was not operational for a period of time while the client device  102   a  was operational or because no capture component existed for a specific type of historical event at the time the event took place. Examples of historical events include the user&#39;s saved word processing documents, media files, presentation documents, calendar entries, spreadsheet documents, the e-mails in a user&#39;s inbox, and the web pages bookmarked by the user. The capture processor  124  can capture historical events by periodically crawling the memory  108  and any associated data storage device for events not previously captured by the capture processor  124 . The capture processor  124  can also capture historical events by requesting certain client applications, such as a web browser or an e-mail application, to retrieve articles and other associated information. For example, the capture processor  124  can request that the web browser application obtain all viewed web pages by the user or request that the e-mail application obtain all e-mail messages associated with the user. These articles may not currently exist in memory  108  or on a storage device of the client device  102   a . For example, the e-mail application may have to retrieve e-mails from a server device. In one embodiment, the search engine  122  indexes historical events. 
     In one embodiment, the capture processor  124  crawls a storage or other type of device associated with the client  102   a - n , such as a hard drive, shared drive or other storage device, transitory device or other device that maintains data, or memory  162 , and locates articles and associated data stored on the storage or other type of device. A “crawl” is defined by some or all of the following activities with respect to a particular device: (1) enumerating some or all articles on the device; (2) sequentially accessing some or all of the articles, reading the article content and metadata, and handing associated information to another component such as an indexing component; (3) analyzing the article content for enumerations of additional articles or devices; (4) ranking the order in which other articles and/or devices are enumerated; and (5) maintaining a record of what articles and/or devices have been previously accessed and/or analyzed to avoid repeating some or all of the previous activities described above. A crawl can locate one or more articles, i.e. a “lightweight” crawl could locate only one article. A storage device can include devices that store data or otherwise maintain data, such as hard drives, shared drives, or transitory devices. 
     The capture processor  124  can identify one or more storage locations on a storage device associated with a client  102   a - n , such as a hard drive or other associated storage device. Each of the storage locations can include one or more articles, such as files, documents, webpages, e-mails, or messages. Storage locations can be identified by locating folders, lists, or other collections of articles on a storage device. Storage locations can also be identified by locating particular articles associated with particular events and/or characteristics. The capture processor  124  can identify these and other storage locations by accessing a storage device associated with a client  102   a - n , such as a hard drive, and searching for these types of storage locations and/or articles. 
     In one embodiment, a capture processor  124  can generate a list of storage locations based on a previous search of a storage device, previously collected information from a crawl of the storage device, or input from or related to a user  112   a - n . For example, utilizing information collected from a previous crawl, such as statistical information associated with articles and a storage device, the capture processor  124  can generate a list of relevant articles such as frequently accessed or recently accessed articles, and identify corresponding storage locations for relevant articles, including folders or directories. 
     The capture processor  124  can rank the storage locations based on events associated with articles, a plurality of types associated with articles, or a combination of events and plurality of types associated with articles. The events, plurality of types associated with articles, and combinations of events and plurality of types associated with articles can be assigned a weight or value, and a corresponding rank for the storage locations can be determined. 
     In another embodiment, the capture processor  124  can identify a plurality of types associated with articles stored in the storage locations. In one embodiment, the capture processor  124  can identify types of articles stored in the storage locations. Types of articles can be identified by analyzing statistical information and/or characteristics associated with articles and a storage device, or can be identified by analyzing the articles stored in the storage locations. 
     In another embodiment, the capture processor  124  can rank the storage locations based at least in part on the types of articles stored within the locations. 
     In another embodiment, the capture processor  124  can rank the storage locations based at least in part on events associated with the articles, and based at least in part on a plurality of types associated with the articles. As described above, the capture processor  124  can identify events associated with articles stored in the storage locations and can also identify a plurality of types of articles stored in the storage locations. As described above, each event can be assigned a weight or a value, and each of the types of articles can also be assigned a weight or value. Further, combinations of events and types of articles can be assigned a weight or value. Using events, types of articles and/or combinations of events and types of articles, corresponding articles and/or storage locations can be ranked accordingly. 
     In any instance, the capture processor  124  can crawl the storage locations based at least in part on the ranking. In this manner, the system  100  can initially crawl storage locations containing relevant articles on a storage device associated with a client device  102   a - n  such as a hard drive, and provide the relevant articles and other content from the storage locations for use in search results or for providing articles and other content for a home page. 
     In the embodiment shown in  FIG. 1 , events captured by the capture processor  124  are sent to the queue  126  in the form of an event schema. The capture processor  124  can also send performance data to the queue  126 . Examples of performance data include current processor load, average processor load over a predetermined period of time, idle time, disk access, the client applications in use, and the amount of memory available. Performance data can also be provided by specific performance monitoring components, some of which may be part of the search engine  122 , for example. The performance data in the queue  126  can be retrieved by the search engine  122  and the capture components of the capture processor  124 . For example, capture components can retrieve the performance data to alter how many events are sent to the queue  126 , or how detailed the events are that are sent (fewer or smaller events when the system is busy), or how frequently events are sent (events are sent less often when the system is busy or there are too many events waiting to be processed). The search engine  122  can use performance data to determine when it indexes various events and when and how often it issues implicit queries. 
     In one embodiment, the queue  126  holds the events and performance data until the search engine  122  is ready to process an event or events. Alternatively, the queue  126  uses the performance data to help determine how quickly to provide the events to the search engine  122 . The queue  126  can comprise one or more separate queues including a user state queue and an index queue. The index queue can queue indexable events, for example. Alternatively, the queue  126  can have additional queues or comprise a single queue. The queue  126  can be implemented as a circular priority queue using memory mapped files. The queue can be a two- or three-priority queue where higher priority events are served before lower priority events, and other components may be able to specify the type of events they are interested in. Generally, real-time events can be given higher priority than historical events, and indexable events can be given higher priority than non-indexable real time events. Other implementations of the queue  126  are possible. In another embodiment, the client device  102   a  does not have a queue  126 . In this embodiment, events are passed directly from the capture processor  124  to the search engine  122 . In other embodiments, events can be transferred between the capture components and the search engine using suitable information exchange mechanisms such as: Extensible Markup Language—Remote Procedure Calling protocol (XML/RPC), Hypertext Transfer Protocol (HTTP), Simple Object Access Protocol (SOAP), shared memory, sockets, local or remote procedure calling, or any other suitable information exchange mechanism. 
     The search engine  122  shown contains an indexer  130 , a query system  132 , and a formatter  134 . The query system  132  can retrieve all real-time events and performance data from the queue  126 . The query system  132  can use performance data and real-time events to update the current user state and generate an implicit query. The query system  132  can also receive and process explicit queries from the user  112   a . Performance data can also be retrieved by the search engine  122  from the queue  126  for use in determining the amount of activity possible by the search engine  122 . 
     In the embodiment shown in  FIG. 1 , indexable real-time events and historical events (indexable events) are retrieved from the queue  126  by the indexer  130 . Alternatively, the queue  126  may send the indexable events to the indexer  130 . The indexer  130  can index the indexable events and can send them to the data store  140  where they are stored. The data store  140  can be any type of computer-readable media and can be integrated with the client device  102   a , such as a hard drive, or external to the client device  102   a , such as an external hard drive or on another data storage device accessed through the network  106 . The data store can be one or more logical or physical storage areas. In one embodiment, the data store  140  can be in memory  108 . The data store  140  may facilitate one or a combination of methods for storing data, including without limitation, arrays, hash tables, lists, and pairs, and may include compression and encryption. In the embodiment shown in  FIG. 1 , the data store comprises an index  142 , a database  144  and a repository  146 . 
     In one embodiment, when the indexer  130  receives an event, the indexer  130  can determine, from the event schema, terms (if any) associated with the event, location information associated with the event (if available), the time of the event (if available), images (if any) associated with the event, and/or any other information defining the event. The indexer  130  can also determine if the event relates to other events and associate the event with related events. For example, for a received instant messenger message event, the indexer can associate the message event with other message events from the same conversation. The messages from the same conversation can be associated with each other in a conversation object, which can be stored in the data store  140 . 
     The indexer  130  can send and incorporate the terms, locations, and times, associated with the event in the index  142  of the data store  140 . The event can be sent to the database  144  for storage and the content of the associated article and any associated images can be stored in the repository  146 . The conversation object associated with e-mail messages can be stored in the database  144 . 
     In the embodiment shown in  FIG. 1 , a user  112   a  can input an explicit query into a search engine interface displayed on the client device  102   a , which is received by the search engine  122 . The search engine  122  can also generate an implicit query based on a current user state, which can be determined by the query system  132  from real-time events. Based on the query, the query system  132  can locate relevant information in the data store  140  and provide a result set. In one embodiment, the result set comprises article identifiers for articles associated with the client applications  120  or client articles. Client articles include articles associated with the user  112   a  or client device  102   a , such as the user&#39;s e-mails, word processing documents, and instant messenger messages, previously viewed web pages and any other article or portion of an article associated with the client device  102   a  or user  112   a . An article identifier may be, for example, a Uniform Resource Locator (URL), a file name, a link, an icon, a path for a local file, or other suitable information that may identify an article. In another embodiment, the result set also comprises article identifiers for articles located on the network  106  or network articles located by a search engine on a server device. Network articles include articles located on the network  106  not previously viewed or otherwise referenced by the user  112   a , such as web pages not previously viewed by the user  112   a.    
     The formatter  134  can receive the search result set from the query system  132  of the search engine  122  and can format the results for output to a display processor  128 . In one embodiment, the formatter  134  formats the results in XML, HTML, or tab delineated text. The display processor  128  can be contained in memory  108  and can control the display of the result set on a display device associated with the client device  102   a . The display processor  128  may comprise various components. For example, in one embodiment, the display processor  128  comprises a Hypertext Transfer Protocol (HTTP) server that receives requests for information and responds by constructing and transmitting Hypertext Markup Language (HTML) pages. In one such embodiment, the HTTP server comprises a scaled-down version of the Apache Web server. The display processor  128  can be associated with a set of APIs to allow various applications to receive the results and display them in various formats. The display APIs can be implemented in various ways, including, for example, DLL exports, COM interface, VB, JAVA, or .NET libraries, or a web service. 
     Through the client devices  102   a - n , users  112   a - n  can communicate over the network  106 , with each other and with other systems and devices coupled to the network  106 . As shown in  FIG. 1 , a server device  150  can be coupled to the network  106 . In the embodiment shown in  FIG. 1 , the search engine  122  can transmit a search query comprised of an explicit or implicit query or both to the server device  150 . The user  112   a  can also enter a search query in a search engine interface, which can be transmitted to the server device  150  by the client device  102   a  via the network  106 . In another embodiment, the query signal may instead be sent to a proxy server (not shown), which then transmits the query signal to server device  150 . Other configurations are also possible. 
     The server device  150  shown includes a server executing a search engine application program, such as the Google™ search engine. In other embodiments, the server device  150  can comprise a related information server or an advertising server. Similar to the client devices  102   a - n , the server device  150  shown can include a processor  160  coupled to a computer-readable memory  162 . Server device  150 , depicted as a single computer system, may be implemented as a network of computer processors. Examples of a server device  150  are servers, mainframe computers, networked computers, a processor-based device, and similar types of systems and devices. The server processor  160  can be any of a number of computer processors, such as processors from Intel Corporation of Santa Clara, Calif. and Motorola Corporation of Schaumburg, Ill. In another embodiment, the server device  150  may exist on a client-device. In still another embodiment, there can be multiple server devices  150 . 
     Memory  162  contains the search engine application program, also known as a network search engine  170 . The search engine  170  can locate relevant information from the network  106  in response to a search query from a client device  102   a . The search engine  170  then can provide the result set to the client device  102   a  via the network  106 . The result set can comprise one or more article identifiers. An article identifier may be, for example, a Uniform Resource Locator (URL), a file name, a link, an icon, a path for a local file, or anything else that identifies an article. In the embodiment shown, an article identifier comprises a URL associated with an article. 
     In one embodiment, the server device  150 , or related device, has previously performed a crawl of the network  106  to locate articles, such as web pages, stored at other devices or systems connected to the network  106 , and indexed the articles in memory  162  or on another data storage device. 
     It should be noted that other embodiments of the present invention may comprise systems having different architecture than that which is shown in  FIG. 1 . For example, in some other embodiments of the present invention, the client device  102   a  is a stand alone device that is not permanently coupled to a network. The system  100  shown in  FIG. 1  is merely exemplary, and is used to explain the exemplary methods shown in  FIGS. 2 through 4 . 
     Processes 
     Various methods in accordance with embodiments of the present invention may be carried out. One exemplary method according to the present invention comprises identifying a plurality of storage locations each comprising a plurality of articles, ranking the plurality of storage locations based at least in part on events associated with the plurality of articles, and crawling the storage locations based at least in part on the ranking. 
     According to another embodiment, a plurality of storage locations is associated with at least one storage device. According to another embodiment a plurality of storage locations is associated with a plurality of storage devices. According to another embodiment a plurality of storage locations comprises at least one of the following: a client side device, a local hard drive, a shared drive, a data storage device. 
     According to another embodiment, events associated with the plurality of articles comprise at least one of the following: user access of an article, user access of a file, user access of a directory associated with an article, user access of a particular type of article, user access of a particular type of file, saving an article to a directory, saving a file to a directory, saving an article to a folder, saving a file to a folder, accessing an article, accessing a file, saving an article, saving a file, opening an article, opening a file, receiving an e-mail from a particular sender, sending an e-mail to a particular recipient, receiving a message from a particular sender, and sending a message to a particular recipient. 
     According to another embodiment, events comprise event data that is obtained from at least one of the following: an operating system, a browser application program, an enabling/monitoring application program, a previous version of a search application program, and a previous version of an application program associated with a search engine application. 
     According to another embodiment, a plurality of articles comprises at least one of the following: a directory, a folder, a file, a webpage, e-mail, a message, and a file associated with an application program. 
     According to another embodiment, identifying a plurality of storage locations comprises at least one of the following: identifying a recently accessed file list, identifying a my favorites list, identifying a my documents folder, identifying a desktop folder, identifying a list of recently accessed files associated with an application program, identifying a folder of recent e-mails, identifying a folder of recent messages, and identifying a cached list of webpages. 
     According to another embodiment, identifying a plurality of storage locations comprises identifying events associated with the plurality of articles. According to another embodiment, events associated with the plurality of articles comprise at least one of the following: user access of an article, user access of a file, accessing an article, accessing a file, saving an article, saving a file, opening an article, and opening a file. 
     According to another embodiment, identifying a plurality of storage locations comprises identifying characteristics associated with the plurality of articles. According to another embodiment, characteristics associated with the plurality of articles comprises at least one of the following: file size, file type, file recency, a date/time of last access, a date/time of recent access, a date/time of last modification, a date/time of creation, a date, and a time. 
     According to another embodiment, a method can comprise identifying a plurality of types of the plurality of articles, wherein ranking the plurality of storage locations comprises ranking the storage locations based at least in part on the plurality of types of the plurality of articles. According to another embodiment, identifying a plurality of types of the plurality of articles comprises prioritizing each of the plurality of types, wherein ranking the storage locations based at least in part on the plurality of types comprises ranking the storage locations based at least in part on the prioritizing each of the plurality of types. 
     According to another embodiment, ranking the plurality of storage locations based at least in part on events associated with the plurality of articles comprises prioritizing the plurality of storage locations based on a frequency of the events. 
     According to another embodiment, ranking the plurality of storage locations based at least in part on events associated with the plurality of articles comprises prioritizing the plurality of storage locations based on a recency of the events. 
     According to another embodiment, crawling the storage locations based at least in part on the ranking comprises crawling a predefined number of the plurality of storage locations, wherein the ranking is prioritized from a highest frequency to a lower frequency. 
     According to another embodiment, crawling the storage locations based at least in part on the ranking comprises at least one of the following: crawling a predefined number of storage locations, crawling a predefined number of directories, crawling a top directory in a file hierarchy, crawling a predefined number of folders, crawling a predefined number of articles, crawling a predefined number of files, crawling a predefined number of indexable files, crawling for a predefined amount of time, and crawling to a predefined depth in a folder hierarchy starting from either a root or a predefined starting folder, or some combination thereof. 
     According to another embodiment, crawling the storage locations based at least in part on the ranking comprises identifying at least one duplicate set of the storage locations, and crawling one of the duplicate set of the storage locations. 
     According to another embodiment, crawling the storage locations based at least in part on the ranking comprises crawling at least some of the storage locations. 
     According to another embodiment, crawling the storage locations based at least in part on the ranking comprises prioritizing the crawling of at least some of the storage locations based at least in part on the ranking. 
     Another exemplary method according to the present invention comprises identifying a plurality of storage locations each comprising a plurality of articles, identifying a plurality of types of the plurality of articles, ranking the plurality of storage locations based at least in part on the plurality of types of the plurality of articles, and crawling the storage locations based at least in part on the ranking. 
     According to another embodiment, a plurality of storage locations is associated with at least one storage device. According to another embodiment, a plurality of storage locations is associated with a plurality of storage devices. 
     According to another embodiment, a plurality of storage locations comprises at least one of the following: a client side device, a local hard drive, a shared drive, a data storage device. 
     According to another embodiment, the plurality of types of the plurality of articles comprise at least one of the following: a file type, a document type, an e-mail type, a message type, and a website domain. 
     According to another embodiment, the plurality of articles comprises at least one of the following: a directory, a folder, a file, a webpage, e-mail, a message, and a file associated with an application program. 
     According to another embodiment, identifying a plurality of storage locations comprises at least one of the following: identifying a recently accessed file list, identifying a my favorites list, identifying a my documents folder, identifying a desktop folder, identifying a list of recently accessed files associated with an application program, identifying a folder of recent e-mails, identifying a folder of recent messages, and identifying a cached list of webpages. 
     According to another embodiment, identifying a plurality of storage locations comprises identifying events associated with the plurality of articles. 
     According to another embodiment, the events comprise event data that is obtained from at least one of the following: an operating system, a browser application program, an enabling/monitoring application program, a previous version of a search application program, and a previous version of an application program associated with a search engine application. 
     According to another embodiment, identifying a plurality of storage locations comprises identifying characteristics associated with the plurality of articles. According to another embodiment, characteristics associated with the plurality of articles comprises at least one of the following: file size, file type, file recency, a date/time of last access, a date/time of recent access, a date/time of last modification, a date/time of creation, a date, and a time. 
     According to another embodiment, ranking the plurality of storage locations based at least in part on the plurality of types of the plurality of articles comprises ranking the plurality of storage locations based at least in part on events associated with the plurality of articles. 
     According to another embodiment, ranking the plurality of storage locations based at least in part on the plurality of types of the plurality of articles comprises prioritizing the plurality of storage locations based at least in part on a frequency of events associated with the plurality of articles. 
     According to another embodiment, ranking the plurality of storage locations based at least in part on the plurality of types of the plurality of articles comprises prioritizing the plurality of storage locations based on a recency of events associated with the plurality of articles. 
     According to another embodiment, crawling the storage locations based at least in part on the ranking comprises crawling a predefined number of the plurality of storage locations, wherein the ranking is prioritized from a highest frequency of events to a lower frequency of events. 
     According to another embodiment, crawling the storage locations based at least in part on the ranking comprises at least one of the following: crawling a predefined number of storage locations, crawling a predefined number of directories, crawling a top directory in a file hierarchy, crawling a predefined number of folders, crawling a predefined number of articles, crawling a predefined number of files, crawling a predefined number of indexable files, crawling for a predefined amount of time, and crawling to a predefined depth in a folder hierarchy starting from either a root or a predefined starting folder, or some combination thereof. 
     According to another embodiment, crawling the storage locations based at least in part on the ranking comprises identifying at least one duplicate set of the storage locations, and crawling one of the duplicate set of the storage locations. 
     According to another embodiment, crawling the storage locations based at least in part on the ranking comprises crawling at least some of the storage locations. 
     According to another embodiment, crawling the storage locations based at least in part on the ranking comprises prioritizing the crawling of at least some of the storage locations based at least in part on the ranking. 
     Another exemplary method according to the present invention comprises identifying a plurality of storage locations each comprising a plurality of articles, identifying a plurality of types of the plurality of articles, ranking the plurality of storage locations based at least in part on the plurality of types of the plurality of articles and based at least in part on events associated with the plurality of articles, and crawling the storage locations based at least in part on the ranking. 
       FIG. 2  illustrates an exemplary method  200  that provides a method for prioritizing a crawl. This exemplary method is provided by way of example, as there are a variety of ways to carry out methods according to the present invention. The method  200  shown in  FIG. 2  can be executed or otherwise performed by any of various systems. The method  200  is described below as carried out by the system  100  shown in  FIG. 1  by way of example, and various elements of the system  100  are referenced in explaining the example method of  FIG. 2 . 
     Referring to  FIG. 2 , the example method  200  begins at block  202 . 
     In block  202 , a plurality of storage locations each comprising a plurality of articles is identified. In the embodiment shown in  FIG. 2 , a capture processor  124  can identify storage locations of articles stored on a storage device associated with a client  112   a - n . Storage locations can be identified by locating folders, lists, or other collections of frequently used and/or frequently accessed articles, files, documents, webpages, e-mails and/or messages on the storage device. The capture processor  124  can identify these and other storage locations by accessing a storage device associated with a client  102   a - n , such as a hard drive, and searching for these types of storage locations. 
     By way of example, a capture processor  124  can identify and access a “my favorites” folder on a hard drive associated with a client device  112   a , and identify the files in the “my favorites” folder. Files in the “my favorite” folder or other similar types of folders can lead to directories and folders containing relevant content for a crawl. The capture processor  124  can then determine a storage location associated with each of the files in the “my favorites” folder. The storage location can include a respective directory and folder where each of the files in the “my favorites” folder are stored on the hard drive. In this manner, the storage locations for the files in the “my favorites” folder can be identified. 
     In another example, a word processing capture component can identify and access a “recently accessed” folder on a hard drive associated with a client device  112   a - n . The word processing capture component can identify the files in the “recently accessed” folder. Files in the “recently accessed” folder or other similar types of folders can lead to directories and folders containing relevant content for a crawl. The word processing capture component can then determine a storage location associated with each of the recently accessed word processing files in the “recently accessed” folder. The storage location can include a respective directory and folder where each of the recently accessed word processing files in the “recently accessed” folder are stored on the hard drive. In this manner, the storage locations for the recently accessed word processing files in the “recently accessed” folder can be identified. 
     In another example, a capture component can access a “history” folder on a hard drive associated with a client device  112   a - n , and identify the webpages recently visited by a particular user. For instance, the capture component can identify articles associated with a web browser application program by identifying webpages in a “history” folder. Webpages in the “history” folder or other similar types of folders can lead to directories and folders containing relevant content for a crawl. The capture processor can then determine a storage location associated with each of the webpages recently visited by a particular user. The storage location can include the Internet or network addresses of the webpages such as the URLs of each of the webpage addresses associated with the webpages recently visited by a particular user. In this manner, storage locations for the webpages recently visited by a particular user can be identified. 
     Different operating systems (OS) can have different sets of relatively important storage locations such as particular folders for storing particular types of articles. For example, in one version of the Microsoft Windows™ OS, there can exist folders such as “My Videos,” “My Music,” “My Pictures,” etc. In a version of the Macintosh OS, there can exist folders such as “Music,” “Pictures,” “Movies,” “Desktop, “Documents,” etc. 
     Storage locations can also be identified by locating particular articles associated with particular events. The capture processor  124  can identify these and other storage locations by accessing a storage device associated with a client  102   a - n , such as a hard drive, and searching for these types of articles. 
     In one example, files can be weighted based on events such as frequency of user access wherein frequently accessed files can be weighted relatively more than less frequently accessed files. Other events can be weighted according to relevance of the particular event to user interest in a particular article. In any instance, the relatively heavier weighted files can be utilized by the capture processor to identify the corresponding storage locations for subsequent processing. 
     In another example, when the capture processor  124  identifies storage locations on a storage device associated with a client  102   a - n , such as a “my favorites” folder, “my documents” folder, and recently accessed list of word processing documents, the capture processor  124  can also identify events associated with articles in the storage locations, such as the frequency of access for each article in the storage locations. The capture processor  124  can determine, for example, the most frequently accessed files in particular storage locations of interest, such as the most frequently accessed files in the “my favorites” folder, “my documents” folder, and recently accessed list of word processing documents. Utilizing the events associated with the files, such as the frequency of access for each of the files, the capture processor  124  can identify frequently accessed files in the storage locations of interest. The corresponding storage locations of the frequently accessed files, including the corresponding directories and folders, can be stored for subsequent processing by the capture processor  124 . In this manner, the storage locations containing relevant content such as frequently accessed files or articles can be identified for subsequent processing. Other events associated with articles can be utilized in accordance with other embodiments of the invention. 
     In another example, the capture processor  124  can identify storage locations by locating articles associated with particular events such as recent user access or use. Recently used and/or accessed articles such as files, documents, webpages, e-mails, or messages on a storage device associated with a client  102   a - n  can lead to relevant content in storage locations on the storage device. When such articles are identified, the capture processor  124  can then identify the corresponding location of such articles, files, documents, webpages, e-mails, or messages on the storage device. 
     In another example, the capture processor  124  can identify relevant articles and storage locations by analyzing previously collected statistical information from a crawl of the storage device associated with a client  102   a - n . For instance, the capture processor  124  can determine frequently used and/or frequently accessed articles based on statistical information from a previous search of the storage device, or based on input from or related to a user  112   a - n . In any instance, the capture processor  124  can generate a list of storage locations based in part on a previous search of a storage device, based in part on previously collected information from a crawl of the storage device, or based in part on input from or related to a user  112   a - n.    
     In another example, utilizing information collected from a previous crawl, such as statistical information associated with articles and a storage device, the capture processor  124  can generate a list of frequently used and/or frequently accessed articles, and identify corresponding storage locations for such articles, including associated folders or directories. 
     Storage locations can also be identified by locating particular articles associated with particular characteristics or types of articles. The capture processor  124  can identify these and other storage locations by accessing a storage device associated with a client  102   a - n , such as a hard drive, and searching for these types of articles. 
     In one example, a word processing capture component can identify word processing application-type files on a storage device associated with a client device  102   a - n . The word processing capture component can identify the storage locations associated with the word processing application-type files, including associated directories and folders. Other types of articles can be utilized to identify storage locations in accordance with other embodiments of the invention, 
     In another example, a word processing capture component can identify a particular type of word processing application file on a storage device associated with a client device  102   a - n , such as “.doc”-type files. The word processing capture component can identify the storage locations associated with the “.doc”-type word processing application files, including associated directories and folders. Other types of articles can be utilized to identify storage locations in accordance with other embodiments of the invention, 
     Storage locations can also be identified by locating particular articles associated with combinations of events and characteristics or types of articles. For example, a capture processor  124  can identify frequently used “.doc”-type word processing application files on a storage device associated with a client  102   a - n . The capture processor  124  can then identify the respective storage locations of such files, including directories and folders. 
     A capture processor  124  can identify storage locations when the search engine  122  becomes operational, on a periodic basis, upon the passage of a predetermined amount of time, inactivity by a user, or in response to certain conditions. In one embodiment, identifying a plurality of storage locations is performed by a crawl capture component of the capture processor  124 . 
     In another embodiment, identifying a plurality of storage locations can be triggered or otherwise initiated by a separate component of the search system. For instance, this may be another capture component of the capture processor  124 . 
     In another embodiment, identifying a plurality of storage locations can be performed by multiple capture components associated with respective applications of the client device, such as the word processing capture component, spreadsheet capture component, e-mail capture component, and presentation capture component. 
     Examples of storage locations associated with articles stored on a storage device associated with a client  102   a - n  can include, but are not limited to, directories on a hard drive associated with client  102   a , shared directories on a shared drive accessible by the client  102   a  via a local area network or network  106 , a directory, a subdirectory, a root directory, a parent directory, a folder, a “my favorites” folder, a desktop folder, a “my documents” folder, a path, a file location, a file name, a database location, a memory location, a drive location, a website, a URL, a network address, and any other suitable location for storing an article. Examples of a storage device accessible by or otherwise associated with a client  102   a - n  include, but are not limited to, a hard drive, a disk drive, a shared drive, a memory device, a database, a removable device, a network, an intranet, a drive accessible by the client device via a local area network, and a drive accessible by the client device via a network. 
     Block  202  is followed by block  204 , in which the plurality of storage locations based at least in part on events associated with the plurality of articles is ranked. In the embodiment shown in  FIG. 2 , the capture processor  124  can rank the plurality of storage locations based at least in part on events associated with the plurality of articles. Various events associated with articles have been previously described above in block  202 . Events can be weighted or otherwise valued according to the relevance each event may have for indicating user interest in a particular article associated with the event. For example, an event that involves direct access (editing or saving) of an article by a user  112   a - n  can be weighted relatively higher than an event that involves indirect access (viewing) of an article by a user  112   a - n , which can be weighted relatively higher than an event that involves only system access (archiving) of an article. In this manner, articles associated with particularly relevant events can be weighted relatively higher or greater than articles associated with less relevant events. Utilizing weights or values associated with events, the capture processor  124  can determine a total weight or value for the corresponding articles, and then determine a rank or ranking for the storage locations associated with the articles. A rank or ranking can include, but is not limited to, a hierarchical order, a numeric score, a weight, a weighted value, a priority, and a prioritized list. 
     In one embodiment, the capture processor  124  can rank storage locations based on one particular type of event associated with the articles. For example, the capture processor  124  can determine the frequency each article has been accessed by a user  112   a - n , and then rank the corresponding storage locations based on the frequency each article has been accessed by a user. In this example, articles that have been accessed with greater frequency can be ranked relatively higher than articles that have been accessed with lesser frequency. 
     In another embodiment, the capture processor  124  can rank storage locations based on two or more particular types of events associated with the articles. For example, the capture processor  124  can determine the frequency each article has been accessed by a user  112   a - n , and then rank the corresponding storage locations based on the frequency each article has been accessed by a user  112   a - n , and based on the recency each article has been accessed by a user  112   a - n . In this example, articles that have been accessed with greater frequency can be ranked relatively higher than articles that have been accessed with lesser frequency, and articles that have been accessed more recently can be ranked relatively higher than articles that have been accessed less recently. In other embodiments, greater or fewer numbers and/or other types of events can be evaluated in accordance with embodiments of the invention. 
     The capture processor  124  can rank any number of storage locations depending on a predefined number, user input, time, the number of storage locations on a particular storage device, or the number of articles on a particular storage device. For example, a predefined number such as “20” can be preselected, and the capture processor  124  can rank the predefined number of storage locations. In another example, a user  112   a - n  can select any number of storage locations, such as selecting “50” via an input device associated with a client device  112   a - n . The capture processor  124  can rank the user input number of storage devices. By way of another example, a capture processor  124  can rank any portion of the total number of storage locations on a particular storage device, such as ranking all of the storage locations for articles contained in particular folders such as a “my favorites” folder, a “my documents” folder, a desktop folder, and/or a “recently accessed” folder. Other embodiments can rank greater or fewer numbers of storage locations in accordance with embodiments of the invention. 
     In any instance, when a list of ranked storage locations to be crawled is identified or otherwise generated, the capture processor  124  can send an event comprising storage location data, including the folder and directory associated with an article in the storage location, to the queue  126 . The indexer  130  can request the storage location data from the queue  126 , and can process and store the storage location data in the data store  140 . The storage location data can be stored in the database  144  or the repository  146  for subsequent retrieval. 
     An event and associated event data can be obtained from, or otherwise collected from, an operating system (OS), a browser application program, or an enabling/monitoring application program. In one embodiment, an event and associated event data can be obtained from a previous version of an application program, such as a search engine application program or an application program associated with a search engine application program, if a particular storage device has been previously crawled. 
     For example, a capture processor  124  or word processing capture component can rank a plurality of storage locations based at least in part on events associated with the plurality of articles, such as ranking directories on a hard drive based in part on the number of times a user accessed word processing files stored on the hard drive. The capture processor  124  or word processing capture component can rank the storage locations associated with the word processing files such as based on the frequency each file was accessed by a particular user. Further, the capture processor  124  or word processing capture component can also rank the storage locations based on a combination of other events. 
     By way of another example, the capture processor  124  can rank the directories associated with the top 20 most frequently accessed files based at least in part on events associated with the articles, such as the number of times each of the top 20 most frequently accessed files has been accessed by a particular user. In this example, a directory associated with a file that has been accessed 100 times by a particular user can be ranked relatively higher than a directory associated with a file that has been accessed 75 times by the particular user, which can be ranked relatively higher than a directory associated with a file that has been accessed 50 times by the particular user. Other types of events can be used to rank storage locations in accordance with other embodiments of the invention. 
     By way of yet another example, the word processing capture component  124  can rank the directories associated with the 20 most frequently accessed word processing files based at least in part on events associated with the articles, such as the number of times each of the top 20 most frequently accessed files has been accessed by a particular user. In this example, a directory associated with a word processing file that has been accessed 100 times by a particular user can be ranked relatively higher than a directory associated with a word processing file that has been accessed 75 times by the particular user, which can be ranked relatively higher than a directory associated with a word processing file that has been accessed 50 times by the particular user. Other types of events can be used to rank storage locations in accordance with other embodiments of the invention. 
     By way of still another example, the capture processor  124  can rank the network addresses or URLs associated with the last 100 webpages visited by a particular user based at least in part on events associated with the webpages, such as the number of times each of the last 100 webpages was visited by a particular user. In this example, a network address or URL associated with a webpage that has been accessed 10 times by a particular user can be ranked relatively higher than a network address or URL associated with a webpage that has been accessed 9 times by the particular user, which can be ranked relatively higher than a network address or URL associated with a webpage that has been accessed 8 times by the particular user. Other types of events can be used to rank storage locations in accordance with other embodiments of the invention. 
     Block  204  is followed by block  206 , in which the storage locations are crawled based at least in part on the ranking. In the embodiment shown in  FIG. 2 , storage locations can be crawled based at least in part on the ranking. A crawl can be performed when the search engine  122  becomes operational, on a periodic basis, upon the passage of a predetermined amount of time, inactivity by a user, or in response to certain conditions. The crawl can be performed by the capture processor  124 . In one embodiment, the crawl is performed by a crawl capture component of the capture processor  124 . 
     In another embodiment, the crawl can be triggered or otherwise initiated by a separate component of the search system. For instance, this may be another capture component of the capture processor  124 . 
     In another embodiment, the crawl can be performed by multiple capture components associated with respective applications of the client device, such as the word processing capture component, spreadsheet capture component, e-mail capture component, and presentation capture component. 
     Furthermore in the example shown in  FIG. 2 , during the crawl, the capture processor  124  can locate articles stored on a storage device associated with a client, such as a hard drive associated with client  102   a , or a shared drive accessible by the client  102   a  via a local area network or network  106 . Other examples of a device accessible by a client  102   a  include, but are not limited to, a removable device, a network, an intranet, a drive accessible by the client device via a local area network, a drive accessible by the client device via a network. 
     The capture processor  124  can determine if each article located has previously been captured or if the article should be captured. Whether the article has changed can be an indication to capture the event. During the crawl, the capture processor  124 , or respective capture component, can determine changes to an article. A change can include, but is not limited to, a modification to the article, addition of keystrokes to the article, new location of the article, deletion of the article, and changing of the article&#39;s metadata or information, including information in additional streams associated with the article, for example, on applicable operating systems. When an article is captured in a crawl, the capture processor  124  can send an event comprising event data to the queue  126 . The indexer  130  can request the event from the queue  126 , and can process and store the event in the data store  140 . The event can be stored in the database  144  for storage and the associated article can be stored in the repository  146 . 
     By way of continuing an example provided above, a capture processor  124  as described in  FIG. 2  can crawl directories associated with the top 20 most frequently accessed files on a hard drive associated with the client device  102   a . Based at least in part on events associated with the top 20 most frequently accessed files, the capture processor  124  can rank the directories in a hierarchical order, such as ranking the directory associated with the most frequently accessed file as first or number “1,” ranking the directory associated with the second most frequently accessed file as second or number “2,” etc. Utilizing the rank of each directory to crawl the directories in a hierarchical order, the capture processor  124  can capture some or all of the files in each of the directories associated with the top 20 most frequently accessed files. In this manner, the directory containing the most frequently accessed file can be crawled first, and the remaining directories containing the other most frequently accessed files can be crawled according to the hierarchical order. Some or all of the files in these directories can be captured and utilized in a search result. 
     By way of continuing another example provided above, a word processing capture component as described in  FIG. 2  can crawl directories associated with the top 20 most frequently accessed word processing files on a hard drive associated with the client device  102   a . Based at least in part on events associated with the top 20 most frequently word processing accessed files, .the word processing capture component can rank the directories in a hierarchical order, such as ranking the directory associated with the most frequently accessed word processing file as first or number “1,” ranking the directory associated with the second most frequently accessed word processing file as second or number “2,” etc. Utilizing the rank of each directory to crawl the directories in a hierarchical order, the word processing capture component can capture some or all of the files in each of the directories associated with the top 20 most frequently accessed word processing files. In this manner, the directory containing the most frequently accessed word processing file can be crawled first, and the remaining directories containing the other most frequently accessed word processing files can be crawled according to the hierarchical order. Some or all of the word processing files in these directories can be captured and utilized in a search result. 
     By way of continuing yet another example provided above, a capture processor  124  can crawl network addresses or URLs stored on a hard drive associated with the client device  102 , wherein the network addresses or URLs are associated with the last 100 webpages visited by a particular user. Based at least in part on events associated with the webpages, such as the number of times each of the last 100 webpages was visited by a particular user, the capture processor  124  can rank the webpages in a hierarchical order, such as ranking the most frequently visited webpage as first or number “1,” ranking the second most frequently visited webpage as second or number “2,” etc. Utilizing the rank of each webpage to crawl the network addresses or URLs associated with the respective webpages in a hierarchical order, the capture processor  124  can capture some or all of the articles at each of the last 100 webpages visited. In this manner, the most frequently visited webpages can be crawled first, and the other remaining frequently visited webpages can be crawled according to the hierarchical order. Some or all of the articles at these webpages can be captured and utilized in a search result. 
     In one embodiment, various parameters of a crawl of storage locations can be defined. For example, a predefined number of storage locations can be crawled, such as crawling 20 storage locations such as the 20 directories containing the 20 most frequently accessed files. By way of another example, a predefined number of directories and/or subdirectories can be crawled, such crawling a top directory in file hierarchy. In another example, a predefined number of folders can be crawled, such as crawling 100 folders containing the 100 most recently accessed word processing files, spreadsheet files, graphics files, or e-mails. In yet another example, a predefined number of files can be crawled, such as crawling the 1000 largest word processing files, or crawling 5000 files regardless of type. By way of another example, a predefined amount of time can be set for a crawl, such as crawling for 1 minute, or crawling for 1 minute every hour. The above parameters can be utilized by a capture processor  124  or other capture component, either individually or in combination with each other, or with other parameters in accordance with other embodiments of the invention. 
     In another embodiment, a capture processor  124  or other capture component can, prior to crawling a storage location, identify at least one duplicate set of storage locations, and crawl one of the duplicate set of the storage locations. By of example, a particular directory can be a sub-tree of multiple directories. The capture processor  124  or other capture component can recognize these instances, and can repress crawling the directory more than once such that processing time can be conserved and efficiently utilized. 
     Some or all of the above examples and associated methods can provide more relevant information to use in subsequent ranking and/or categorization of articles. In the embodiment shown in  FIG. 2 , the capture processor  124  can transmit the collected articles and other information to the database  144 , or other data storage device for storage and subsequent retrieval. In one embodiment, the capture processor  124  can include the collected articles and other information with event data when compiling an event. In another example, a word processing capture component can transmit the collected articles and other information for word processing documents to be stored in the database  144 . The search engine  122  can utilize some or all of the collected articles and/or other information in ranking or otherwise providing articles in response to a search. In another embodiment, the search engine  122  can utilize some or all of the collected articles and/or other information for selecting relevant articles for a home page. In either instance, the search engine  122  can retrieve some or all of the collected articles and/or other information from the database  144  and can utilize the collected articles and/or other information in an algorithm for ranking articles in a search result, for selecting articles for a home page, for deciding a device to crawl next, determining a frequency to re-crawl, or any combination thereof. In another example, some or all of the collected articles and/or other information can be output to a user for display. The search engine  122  can call to the display processor  128  to output some or all of the collected articles and/or other information for viewing by a user  112   a - n  with a display interface associated with a client device  102   a - n . Some or all of the collected articles and/or other information can be retrieved from the database  144 , and output to the display interface for viewing. 
     In block  206 , the method  200  ends. 
       FIG. 3  illustrates an exemplary method  300  that provides another method for prioritizing a crawl. This exemplary method is provided by way of example, as there are a variety of ways to carry out methods according to the present invention. The method  300  shown in  FIG. 3  can be executed or otherwise performed by any of various systems. The method  300  is described below as carried out by the system  100  shown in  FIG. 1  by way of example, and various elements of the system  100  are referenced in explaining the example method of  FIG. 3 . 
     The example method  300  begins at block  302 . 
     In block  302 , a plurality of storage locations each comprising a plurality of articles is identified. Similar to the embodiment shown and described above in block  202  of  FIG. 2 , the capture processor  124  of the embodiment shown in  FIG. 3  can identify storage locations of articles stored on a storage device associated with a client device  112   a - n.    
     Block  302  is followed by block  304 , in which a plurality of types of the plurality of articles is identified. Various types of articles can be identified by a capture processor  124  by determining characteristics associated with the articles of interest including, but not limited to, file type, file size, file recency, a date/time of last access, a date/time of recent access, a date/time of last modification, a date/time of creation, a date, and a time. Other types of articles can be identified in accordance with other embodiments of the invention. 
     In some instances, characteristics associated with articles of interest can be dependent on a particular type of operating system (OS) such as Microsoft Windows™ or Apple Macintosh™ OS. By way of example, in a version of the Microsoft Windows™ OS, characteristics associated with articles of interest can include, but are not limited to, date/time of last access, a date/time of recent access, a date/time of last modification, and date/time of creation. 
     For example, articles such as a documents can be distinguished by application programs the documents are associated with. Various application programs such as word processing, spreadsheet, presentation, and graphics application programs can generate different types of articles such as files with different suffixes. In another example, a particular type of application program, such as word processing programs, can generate different types of articles depending on the manufacturer or distributor of the word processing program. Different types of word processing programs can generate different types of articles such as files with different suffixes. In yet another example, types of articles can be distinguished by file size, such as a predefined ranges of file size distinguishing between large, medium, and small files. In another example, types of articles can be distinguished by a time or date associated with the article, such as files created within the last week, month, year, or any other period of time. In this manner, recently created or saved articles, for example, can be identified. 
     In one embodiment, a word processing capture component can identify one or more particular types of articles associated with various word processing application programs, such as distinguishing between different types of word processing files by their respective suffixes “.wpd,” “.doc,” and “.txt.” The word processing capture processor can then determine a storage location associated with each type of word processing files, such as “.doc”-type files. In any instance, the storage location can include the directory and folder where each type of word processing files are stored. In this manner, the storage locations for each type of word processing files can be identified. 
     By way of another example, a capture component can identify particular types of webpages in a “history” or “recently viewed” folder. For instance, the capture component can identify, determine, or otherwise distinguish between, any type of webpage in a “history” or “recently viewed” folder, such as “.com”-type, “.org”-type, and “.net”-type webpages. In one embodiment, the capture component can determine the types of webpages visited by a particular user and stored in a “history” folder, such as which of the webpages are “.com”-type webpages. 
     Block  304  is followed by block  306 , in which the plurality of storage locations based at least in part on the plurality of types of articles is ranked. In the embodiment shown in  FIG. 3 , the capture processor  124  can rank the plurality of storage locations based at least in part on the plurality of types of articles. Various types of articles have been previously described above in block  304 . The types associated with particular articles can be weighted or otherwise valued according to the relevance each type of article may have for indicating user interest in a particular type of article. For example, a word processing application program-type article that can be accessed directly by a user  112   a - n  can be weighted relatively higher than a dynamic link library (DLL)-type article which is rarely accessed directly by a user  112   a - n . In another example, recently created articles such as files created within the past year can be weighted relatively higher than articles created between a year and two years ago, which can be weighted relatively higher than articles created over two years ago. In this manner, particularly relevant types of articles can be weighted relatively higher or greater than less relevant types of articles. Utilizing weights or values associated with types of articles, the capture processor  124  can determine a total weight or value for the corresponding articles, and then determine a rank or ranking for the storage locations associated with the articles. A rank or ranking can include, but is not limited to, a hierarchical order, a numeric score, a weight, a weighted value, a priority, and a prioritized list. 
     For example in  FIG. 3 , a capture processor  124  or word processing capture component can rank a plurality of storage locations based at least in part on the plurality of types of articles, such as ranking directories on a hard drive based in part on the number of “.doc”-type word processing files versus the number of “.txt”-type word processing files. The capture processor  124  or word processing capture component can also rank the storage locations associated with one particular type of article, such as “.doc”-type word processing files, based on the frequency the particular type of file is present in a set or group of interest. Further, the capture processor  124  or word processing capture component can also rank the storage locations based on weighting a combination of other types of articles. For instance, the capture processor  124  or word processing capture component can rank the storage locations associated with the frequency of “.doc”-type word processing files in those locations in a first ranking order, then rank the storage locations associated with the frequency of another type of word processing files in those locations, such as “.txt”-type files in a second ranking order, and then rank the storage locations associated with the frequency of another type of word processing files in those locations, such as “.wpd”-type files in a third ranking order. In some instances, particular types of articles, such as text-type documents, can be batch processed. In one example, text-type documents such as “.doc,” “.dot,” “.txt,” and “.rtf”-type files can be separately identified, but ranked collectively as a single batch or group. The single batch or group can then be ranked or otherwise prioritized against other batches, groups, or types of articles, files, or documents. Other rankings, types of articles, articles, weighting of rankings, types of articles, and/or articles can exist in other embodiments of the invention. 
     By way of another example, the capture processor  124  can rank storage locations such as directories on a storage device based at least in part on the plurality of types of articles in the directories, such as the number of “.doc”-type word processing files in a recently accessed word processing application program file folder. In this example, “.doc”-type files may be the most prevalent with 16 of the 20 recently accessed word processing application program files, while only 3 of the 20 recently accessed word processing application program files are “.txt”-type files, and 1 of the 20 recently accessed files is a “.wpd”-type file. Directories associated with each “.doc”-type file can be ranked relatively higher than directories associated with the “.txt”-type files that are less prevalent in the 20 recently accessed word processing application program files, which can be ranked relatively higher than a directory associated with a “.wpd”-type file that is even less prevalent in the 20 recently accessed word processing application program files. Other combinations of types of articles can be used to rank storage locations in accordance with other embodiments of the invention. 
     By way of another example, the capture processor  124  can rank the network addresses or URLs associated with the 100 webpages recently visited by a particular user based at least in part on the types of webpages, such as the number of “.com”-type webpages versus the number of “.org”-type webpages. The capture processor  124  can also rank the storage locations associated with one particular type of article, such as “.com”-type webpages, based on the frequency of the type of webpage in a set or group of interest. Further, the capture processor  124  can also rank the storage locations based on weighting a combination of other types of articles. For instance, the capture processor  124  can rank the storage locations associated with the frequency of “.com”-type webpages in those locations in a first ranking order, can rank the storage locations associated with the frequency of “.org”-type webpages in those locations in a second ranking order, and can rank the storage locations associated with the frequency of “.net”-type webpages in those locations in a third ranking order. Further, the capture processor  124  can also rank the storage locations based on any combination of other types of articles. Other rankings, types of articles, articles, weighting of rankings, types of articles, and/or articles can exist in other embodiments of the invention. 
     Block  306  is followed by block  308 , in which the storage locations are crawled based at least in part on the ranking. Similar to the embodiment described and shown above in  206  of  FIG. 2 , the capture processor  124  of the embodiment shown in  FIG. 3  can crawl storage locations based at least in part on the ranking. As described above in block  206 , various parameters of a crawl of storage locations can be defined. 
     By way of example, a capture processor  124  as described in  FIG. 3  can crawl directories associated with particular types of articles, such as “.doc”-type and “.txt”-type files, in the recently accessed word processing application program file folder on a hard drive associated with the client device  102   a . Based at least in part on the types of articles in the recently accessed word processing application program file folder, the capture processor  124  can rank the directories in a hierarchical order, such as ranking a directory associated with “.doc”-type files as first or number “1,” ranking the directory associated with the second most prevalent type of file as second or number “2,” etc. Utilizing the rank of each directory to crawl the directories in a hierarchical order, the capture processor  124  can capture some or all of the files in each of the directories associated with the recently accessed word processing application program files. In this manner, the directory containing a particular type of file that is among the most frequently accessed files can be crawled first, and the remaining directories containing other types of files among the most frequently accessed files can be crawled according to the hierarchical order. Some or all of the files in these directories can be captured and utilized in a search result. 
     By way of another example, a capture processor  124  can crawl network addresses or URLs stored on a hard drive associated with the client device  102 , wherein the network addresses or URLs are associated with the recently visited webpages of a particular type, such as “.com”-type webpages, visited by a particular user. Based at least in part on the types of articles, such as the number of “.com”-type webpages, the capture processor  124  can rank the “.com”-type webpages in a hierarchical order, such as ranking the most prevalent type of webpages, “.com”-type webpages, as first or number “1,” ranking the second most prevalent type of webpages, “.org”-type webpages, as second or number “2,” etc. Utilizing the rank of each type of webpage to crawl the network addresses or URLs associated with the respective webpages in a hierarchical order, the capture processor  124  can capture some or all of the articles at each of the “.com”-type webpages recently visited. In this manner, the most prevalent type of webpages recently visited, “.com”-type webpages, can be crawled first, and the other remaining prevalent types of web pages recently visited, “.org”-type webpages, etc. can be crawled according to the hierarchical order. Some or all of the articles at these webpages can be captured and utilized in a search result. 
       FIG. 4  illustrates an exemplary method  400  that provides another method for prioritizing a crawl. This exemplary method is provided by way of example, as there are a variety of ways to carry out methods according to the present invention. The method  400  shown in  FIG. 4  can be executed or otherwise performed by any of various systems. The method  400  is described below as carried out by the system  100  shown in  FIG. 1  by way of example, and various elements of the system  100  are referenced in explaining the example method of  FIG. 4 . 
     The example method  400  begins at block  402 . 
     In block  402 , a plurality of storage locations each comprising a plurality of articles is identified. Similar to the embodiment shown and described above in block  202  of  FIG. 2  and in block  302  of  FIG. 3 , the capture processor  124  of the embodiment shown in  FIG. 4  can identify storage locations of articles stored on a device associated with a client device. 
     Block  402  is followed by block  404 , in which a plurality of types of the plurality of articles is identified, similar to the embodiment described and shown above in block  304  in  FIG. 3 . 
     Block  404  is followed by block  406 , in which the plurality of storage locations is ranked based at least in part on events associated with the plurality of articles, and based at least in part on the plurality of types of the plurality of articles. In the embodiment shown in  FIG. 4 , the capture processor  124  can rank the plurality of storage locations based at least in part on events associated with the plurality of articles, and based at least in part on the plurality of types of the plurality of articles. As previously described above, a rank or ranking can include, but is not limited to, a hierarchical order, a numeric score, a weight, a weighted value, a priority, and a prioritized list. 
     For example in  FIG. 4 , a capture processor  124  or word processing capture component can rank a plurality of storage locations based at least in part on events associated with the plurality of articles, and based at least in part on the plurality of types of articles. In this example, the capture processor  124  can rank directories on a hard drive based in part on the number of times a user accessed “.doc”-type word processing files stored on the hard drive versus the number of times a user accessed “.txt”-type word processing files stored on the hard drive. The capture processor  124  or word processing capture component can also rank the storage locations based on a particular type of article, such as “.doc”-type word processing files, and further based on the frequency each particular type of file was accessed by a particular user. Ultimately, the capture processor  124  or word processing capture component can rank the storage locations based on the frequency of access for all of the “.doc”-type word processing files in a first ranking order, then rank the storage locations based on the frequency of access for all of another type of word processing files, such as “.txt”-type files in a second ranking order, and then rank the storage locations based on the frequency of access for all of yet another type of word processing documents, such as “.wpd”-type files in a third ranking order. Moreover, the capture processor  124  and/or word processing capture component can also rank the storage locations first based on the frequency of access, and then based on a number of the particular types of files in each directory. For instance, the capture component  124  can rank directories based on frequency of access, and then the word processing capture component can modify the rank of the directories based on the prevalence of particular types of word processing files in those directories. In this example, various weighting methodologies can be applied depending on weighting of a combination of events associated with articles as well as the types of articles. Furthermore, a capture processor  124  or word processing capture component can also rank the storage locations based on any combination of other types of articles, and/or events associated with the articles in accordance with other embodiments of the invention. 
     By way of another example, the capture processor  124  can rank the directories associated with the top 20 most frequently accessed files based at least in part on the plurality of types of articles, such as the number of times each of the top 20 most frequently accessed “.doc”-type word processing files has been accessed by a particular user. In this example, a directory associated with a “.doc”-type file that has been accessed 100 times by a particular user can be ranked relatively higher than a directory associated with a “.doc”-type file that has been accessed 75 times by the particular user, which can be ranked relatively higher than a directory associated with a “.doc”-type file that has been accessed 50 times by the particular user. In another example, a directory with a top 20 frequently accessed “.doc”-type file can be ranked relatively higher than a directory with a top 20 frequently accessed “.txt”-type file, which can be ranked relatively higher than a directory with a top 20 frequently accessed “.wpd”-type file. Other combinations of types of articles can be used to rank storage locations in accordance with other embodiments of the invention. 
     By way of yet another example, the capture processor  124  can rank the network addresses or URLs associated with the last 100 webpages visited by a particular user based at least in part on the types of webpages, such as the number of times each of the “.com”-type webpages was visited by a particular user versus the number of times each of the “.org”-type webpages was visited by the particular user. The capture processor  124  can also rank the storage locations associated with one particular type of article, such as “.com”-type webpages, based on the frequency each article was accessed by a particular user. For instance, a network address or URL associated with a “.com”-type webpage that has been accessed 10 times by a particular user can be ranked relatively higher than a network address or URL associated with a “.com”-type webpage that has been accessed 9 times by the particular user, which can be ranked relatively higher than a network address or URL associated with a “.com”-type webpage that has been accessed 8 times by the particular user. Further, the capture processor  124  can also rank the storage locations based on any combination of other types of articles. For instance, the capture processor  124  can rank the storage locations associated with the “.com”-type webpages in a first ranking order, then rank the storage locations associated with another type of article, such as “.org”-type webpages in a second ranking order, and then rank the storage locations associated with another type of article, such as “.net”-type webpages in a third ranking order. Other combinations of types of articles can be used to rank storage locations in accordance with other embodiments of the invention. 
     Block  406  is followed by block  408 , in which the storage locations are crawled based at least in part on the ranking. Similar to the embodiment described and shown above in block  206  of  FIG. 2  and in block  308  of  FIG. 3 , the capture processor  124  of the embodiment shown in  FIG. 4  can crawl storage locations based at least in part on the ranking. As described above in block  206  and block  308 , various parameters of a crawl of storage locations can be defined. 
     By way of example, a capture processor  124  as described in  FIG. 4  can crawl directories associated with the top 20 most frequently accessed files on a hard drive associated with the client device  102   a . Based at least in part on events associated with the top 20 most frequently accessed files, the capture processor  124  can rank the directories in a hierarchical order, such as ranking the directory associated with the most frequently accessed file as first or number “1,” ranking the directory associated with the second most frequently accessed file as second or number “2,” etc. Utilizing the rank of each directory to crawl the directories in a hierarchical order, the capture processor  124  can capture some or all of the files in each of the directories associated with the top 20 most frequently accessed files. In this manner, the directory containing the most frequently accessed file can be crawled first, and the remaining directories containing the other most frequently accessed files can be crawled according to the hierarchical order. Some or all of the files in these directories can be captured and utilized in a search result. 
     By way of another example, a word processing capture component as described in  FIG. 4  can crawl directories associated with the top 20 most frequently accessed word processing files of a particular type, such as “.doc”-type files, on a hard drive associated with the client device  102   a . Based at least in part on the types of articles associated with the top 20 most frequently word processing accessed files of a particular type, the word processing capture component can rank the directories in a hierarchical order, such as ranking the directory associated with the most frequently accessed word processing file as first or number “1,” ranking the directory associated with the second most frequently accessed word processing file as second or number “2,” etc. Utilizing the rank of each directory to crawl the directories in a hierarchical order, the word processing capture component can capture some or all of the files in each of the directories associated with the top 20 most frequently accessed word processing files of a particular type. In this manner, the directory containing the most frequently accessed word processing file can be crawled first, and the remaining directories containing the other most frequently accessed word processing files can be crawled according to the hierarchical order. Some or all of the word processing files in these directories can be captured and utilized in a search result. 
     By way of yet another example, a capture processor  124  can crawl network addresses or URLs stored on a hard drive associated with the client device  102 , wherein the network addresses or URLs are associated with the last 100 webpages of a particular type, such as “.com”-type webpages, visited by a particular user. Based at least in part on the types of articles, such as the number of times each of the last 100 “.com”-type webpages was visited by a particular user, the capture processor  124  can rank the “.com”-type webpages in a hierarchical order, such as ranking the most frequently visited “.com”-type webpage as first or number “1,” ranking the second most frequently visited “.com”-type webpage as second or number “2,” etc. Utilizing the rank of each webpage to crawl the network addresses or URLs associated with the respective webpages in a hierarchical order, the capture processor  124  can capture some or all of the articles at each of the last 100 “.com”-type webpages visited. In this manner, the most frequently visited “.com”-type webpage can be crawled first, and the other remaining frequently visited “.com”-type webpages can be crawled according to the hierarchical order. Some or all of the articles at these webpages can be captured and utilized in a search result. 
     The method  400  ends at block  408 . 
     While the above description contains many specifics, these specifics should not be construed as limitations on the scope of the invention, but merely as exemplifications of the disclosed embodiments. Those skilled in the art will envision many other possible variations that are within the scope of the invention.