Source: http://www.google.de/patents/US6098064
Timestamp: 2013-06-19 11:19:35
Document Index: 265188079

Matched Legal Cases: ['Application No. 08', 'Application No. 08', 'Application No. 09', 'Application No. 09', 'Application No. 09', 'Application No. 09']

Patent US6098064 - Prefetching and caching documents according to probability ranked need S list - Google PatenteSuche Bilder Maps Play YouTube News Gmail Drive Mehr » Erweiterte Patentsuche | Webprotokoll | Anmelden Erweiterte Patentsuche PatenteA method is presented for determining whether to prefetch and cache documents on a computer. In one embodiment, documents are prefetched and cached on a client computer from servers located on the Internet in accordance with their computed need probability. Those document with a higher need probability...http://www.google.de/patents/US6098064?utm_source=gb-gplus-sharePatent US6098064 - Prefetching and caching documents according to probability ranked need S list Ver�ffentlichungsnummerUS6098064 APublikationstypErteilung Anmeldenummer09/083,645 Ver�ffentlichungsdatum1. Aug. 2000Eingetragen22. Mai 1998 Priorit�tsdatum22. Mai 1998 Ver�ffentlichungsnummer083645, 09083645, US 6098064 A, US 6098064A, US-A-6098064, US6098064 A, US6098064A ErfinderPeter L. Pirolli, James E. PitkowUrspr�nglich Bevollm�chtigterXerox CorporationPatentzitate (4), Nichtpatentzitate (22), Referenziert von (190), Klassifizierungen (20) Externe Links: USPTO, USPTO-Zuordnung, EspacenetPrefetching and caching documents according to probability ranked need S listUS 6098064 A Zusammenfassung A method is presented for determining whether to prefetch and cache documents on a computer. In one embodiment, documents are prefetched and cached on a client computer from servers located on the Internet in accordance with their computed need probability. Those document with a higher need probability are prefetched and cached before documents with lower need probabilities. The need probability for a document is computed using both a document context factor and a document history factor. The context factor of the need probability of a document is determined by computing the correlation between words in the document and a context Q of the operating environment. The history factor of the need probability of a document is determined by integrating both the recency of document use and the frequency of document use.
What is claimed is: 1. A method for determining with a computer whether to prefetch and cache documents, comprising the steps of:recording context data and history data of the documents in a needs list stored in a memory of the computer; said recording step being performed each time a document request is received by the computer; computing a need probability for the documents recorded in the needs list stored in the memory; said computing step computing the need probability for the documents as a function of the recorded context data and history data of the documents; and identifying those documents in the needs list with the greatest computed need probability to be prefetched and cached in the memory of the computer. 2. The method according to claim 1, wherein said step of recording history data further comprises the steps of:recording in the needs list how recently each document is referenced; and recording in the needs list how frequently each document is referenced. 3. The method according to claim 2, wherein said step of recording context data further comprises the steps of:recording keywords of each document referenced in the needs list; and recording links of each document referenced in the needs list. 4. The method according to claim 3, wherein said computing step further comprises the steps of:estimating a history factor for predicting how likely each document recorded in the needs list is to be needed; and estimating a context factor for predicting how relevant each document in the needs list is given a context Q of an operating environment. 5. The method according to claim 4, wherein said computing step computes the need probability for each document in the needs list by summing the weighted logarithms of the estimated history factor and the estimated context factor.
6. The method according to claim 4, wherein said estimating step estimates the history factor according to the following equation: ##EQU4## where, ##EQU5## sets forth a predicted needs odds for a document per day; D.sub.i sets forth how many days since a document was last accessed;F.sub.i sets forth how frequently a document is accessed over a period of days; and αβγ set forth parameters that are estimated from a population of documents. 7. The method according to claim 4, further comprising the step specifying the context Q using a pre-identified set of documents.
17. The method according to claim 15, wherein said computing step computes the need probability for each document in the needs list using the following equation: ##EQU6## where, ##EQU7## sets forth a predicted needs odds for a document per day; D.sub.i sets forth how many days since a document was last accessed;F.sub.i sets forth how frequently a document is accessed over a period of days; and αβγ set forth parameters that are estimated from a population of documents. 18. An apparatus for determining whether to prefetch and cache documents, comprising:a memory for storing a needs list of documents; means for recording context data and history data of the documents in the needs list; said recording means recording the context data and the history data when a document request is received; means for computing a need probability for the documents recorded in the needs list; said computing means computing the need probability for the documents as a function of the recorded context data and history data of the documents; and means for identifying those documents in the needs list with the greatest computed need probability to be prefetched and cached in said memory. 19. The apparatus according to claim 18, wherein said recording means further comprises:means for recording, in the needs list, how recently each document is referenced; and means for recording, in the needs list, how frequently each document is referenced. 20. The apparatus according to claim 18, wherein said recording means further comprises:means for recording, in the needs list, keywords of each document referenced; and means for recording, in the needs list, links of each document referenced. Beschreibung
SUMMARY OF THE INVENTION In accordance with the invention there is provided a method, and apparatus therefor, for determining whether to prefetch to a client computer documents stored on server computers (hereinafter servers). In addition, the method is suitable for determining whether to prefetch and cache documents at servers or proxy servers. The servers may, for example, be electronic document repositories located on the Internet.
FIG. 2 illustrates a detailed block diagram of the client computers 102 in which a prefetch and cache module 202 for carrying out the present invention operates. The prefetch and cache module 202 (hereinafter the "P&C module") forms one of a plurality of modules that are executed as needed by an internet client (or browser) 204. Examples of internet clients include the Navigator� developed by Netscape Communications Corporation and the Explorer developed by Microsoft Corporation. The internet client 204 operates on a conventional operating system 206 such as Windows NT Computer Incorporated. The operating system 206 includes a cache manager 207, which is described in more detail below. Cache manager 207 stores and retrieves data from cache memory 208. The cache memory 208 includes RAM memory 210 and disk memory 212. It will be appreciated by those skilled in the art, however, that cache memory 208 may in addition include flash memory, floppy disk, or any other form of optical or magnetic storage.
Steps 708 and 710 concern the manner in which the history factor (P.sub.H) and the context factor (P.sub.C) are computed. How the history and context factors are computed relates to the manner in which to define a need probability that is a function of both the history of events relating to documents used by a client computer and the relationship of the current context of those documents and others stored on the Internet. The motivation for defining a need probability that combines a history and a context factor is to accelerate the delivery of information to users.
At step 708, the history factor for the current document is estimated. In one embodiment, the history factor is approximated empirically using the expected values of the need probabilities of the history factor (P.sub.H), which can otherwise be represented using a needs odds (P/(1-P)). Accordingly, the predicted needs odds of the history factor for a particular document (i) can be approximated empirically using the following power function: ##EQU1## where, ##EQU2## is the predicted needs odds for a document per day; D.sub.i is the number of days since the document was last accessed (i.e., recency);
F.sub.i is the frequency of accesses of the document over a period of days; and
In accordance with one aspect of the invention, the power function provides a single value which integrates both recency and frequency of document use. This single value can be used on its own to represent document need. More specifically, the power function provides a method for determining whether documents should be cached by analyzing the history of document use over time. Computing the history factor as defined above is computed using the vector of times referenced 512 to determine the number of days since the document was last accessed (i.e., recency D.sub.i) and the frequency of access of the document over a period of days (i.e., frequency or F.sub.i). Once these two values are determined, the predicted needs odds for a document per day can be computed using the equation set forth above.
Once the context Q is defined for a client computer 102, the probability that a document has a relevant context is estimated. The estimate of the probability of a document being relevant given the context Q is defined as the document's context factor (P.sub.C). In one embodiment, the context factor (P.sub.C) is computed using inter-word correlations. That is, the context factor (P.sub.C) is computed by examining how closely words defining the context Q are correlated to words in the vector of keywords 516 for a document. One method for computing inter-word correlations is disclosed by Schuetze, in "Dimensions of Meaning," Proceedings of the Supercomputing 1992, pp. 787-796, Minneapolis, Minn., which is herein incorporated by reference.
In another embodiment, the context factor (P.sub.C) is computed using spreading activation networks (i.e., log likelihood's). Spreading activation networks arrange web pages as nodes in graph networks that represent usage, content, and hypertext relations among web pages. An example of spreading activation is disclosed in U.S. Pat. No. 5,835,905 (entitled: "System For Predicting Documents Relevant To Focus Documents By Spreading Activation Through Network Representations Of A Linked Collection Of Documents"), which is incorporated herein by reference. In yet another embodiment, the context factor (P.sub.C) is computed using vector similarities from co-citation or clustering analysis. Co-citation analysis involves identifying how many times pairs of documents are cited together. An example of co-citation analysis is disclosed in U.S. Pat. No. 6,038,574 (entitled: "Method And Apparatus For Clustering A Collection Of Linked Documents Using Co-Citation Analysis"), which is incorporated herein by reference.
At step 712, a need probability for the document is computed and stored in the document data structure 504 as computed need probability variable 511. In the preferred embodiment, the need probability for a document is a combination of both the history factor estimated at step 708 and the context factor estimated at step 710. As set forth above, a document's need probability (P.sub.T) is computed at step 712 using both a history factor (P.sub.H) and a context factor (P.sub.C). In accordance with one aspect of the invention, the document need probability (P.sub.T) is computed by summing the weighted logarithms of the estimated history factor (P.sub.H) and the estimated context factor (P.sub.C) using the following equation:
P.sub.T =w.sub.1 log (P.sub.H)+w.sub.2 log (P.sub.C),
w.sub.1 and w.sub.2 are weights;
P.sub.H is the predicted need for a document per day; and
P.sub.C is the predicted relevance of a document.
In a preferred embodiment, the weights w.sub.1 and w.sub.2 are equal so that the history factor and the context factor contribute equally to the needs probability (P.sub.T). It will be appreciated by those skilled in the art, however, that the weights w.sub.1 and w.sub.2 can be defined so that disproportionate percentages of the history factor and the context factor document are used to define the needs probability P.sub.T. In another embodiment, the weight w.sub.2 is set equal to zero, thereby providing a predicted need probability for a document that is solely based on the document's recency and frequency of use. In yet another embodiment, the other data 510, such as document retrieval time 520 and document size 522, is factored into the equation for computing document need probability (P.sub.T).
The method set forth above for prefetching and caching documents at a client computer can be readily scaled to operate on a proxy server 112 or server 104. However, unlike the P&C module 202 in the client computer 102, the P&C module 202 in the proxy server 112 (shown in FIG. 3) and server 104 (shown in FIG. 5) compute a collective context Q for a community of client computers as opposed to an individual context Q for a single client computer. The collective context Q is computed by pooling many client contexts together. In one embodiment, the collective context is defined by computing the union set of all of the individual contexts Q.sub.1, Q.sub.2, Q.sub.3 . . . Q.sub.n of individual client fetch requests. This collective context Q is then applied across all users of a server or proxy server. In operation, the P&C module 202 in the proxy server 112 and the server 104 record a needs list for the community of clients making requests for documents to the server using an HTTP module 214 and an FTP module 304.
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Aug. 2008Gennaro CuomoMethod, system and program product for identifying caching opportunities* Vom Pr�fer zitiertKlassifizierungen US-Klassifikation1/1, 707/E17.12, 707/999.2, 707/999.5Internationale KlassifikationG06F17/30, H04L29/06, H04L29/08 UnternehmensklassifikationH04L29/08819, H04L29/06, G06F17/30902, H04L29/0881, H04L29/08666, H04L29/08702, H04L29/08072 Europ�ische KlassifikationG06F17/30W9C, H04L29/06, H04L29/08N19, H04L29/08N27, H04L29/08N27S4, H04L29/08N27S2DrehenOriginalbildGoogle-Startseite - Sitemap - USPTO-Bulk-Downloads - Datenschutzerkl�rung - Nutzungsbedingungen - �ber Google Patente - Feedback gebenDaten bereitgestellt von IFI CLAIMS Patent Services.© 2012 Google