Method and system for processing web activity data

• US 9,576,251 B2
• Filed: 11/13/2009
• Issued: 02/21/2017
• Est. Priority Date: 11/13/2009
• Status: Active Grant

• Current Assignee: Micro Focus LLC (Open Text Corporation)
• Original Assignee: Hewlett Packard Enterprise Development LP (Hewlett-Packard Enterprise Company)
• Inventors: Forman, George, Kirshenbaum, Evan R., Rajaram, Shyam Sundar
• Primary Examiner(s): Burke, Jeffrey A
• Assistant Examiner(s): VU, THONG H
• Application Number:
  US12/618,151
• Publication Number:
  US 20110119267A1
• Time in Patent Office: 2,657 Days
• Field of Search: 707/706, 707/3, 707/102, 707/607, 707/708, 707/10, 707/6, 707/2, 707/805, 707/5, 707/736, 707/748, 707/760, 709/224
• US Class Current: 1/1
• CPC Class Codes: G06Q 10/00 Administration; Management
• CPC Class Codes: G06Q 30/00 Commerce

Abstract

The present disclosure provides a computer-implemented method of processing Web activity data. The method includes obtaining a collection of Web activity data generated by a plurality of users at a plurality of Webpages, wherein the Webpages are from a plurality of unaffiliated Websites. The method also includes extracting a plurality of search terms from the Web activity data and associating each of the plurality of search terms with a corresponding Webpage. The method also includes generating statistical data from the Web activity data based, at least in part, on the search terms, the statistical data corresponding to the online activity at one or more Webpages.

Claims

1. A computer-implemented method of processing Web activity data, comprising:

obtaining a collection of Web activity data generated by a plurality of users at a plurality of Webpages associated with a plurality of unaffiliated Websites, wherein the Web activity data comprises a plurality of query Uniform Resource Locators (URLs) that represent searches performed by users;

extracting a plurality of search terms from the plurality of query URLs and associating each of the plurality of search terms with a corresponding Webpage, wherein extracting the plurality of search terms comprises extracting a target class of information from a URL query field that has been identified by a classifier generated by a training system; and

generating statistical data from the Web activity data based, at least in part, on the search terms, wherein the statistical data comprises a first statistical data for a target search term associated with a target Webpage and a second statistical data for the target search term associated with another Webpage.

2. The computer-implemented method of claim 1, wherein generating the statistical data comprises using the target search term to identify at least one Webpage at which the target search term occurs.

3. The computer implemented method of claim 1, wherein generating the statistical data comprises maintaining a set of counters, each counter corresponding to the target search term and one of the Webpages in the Web activity data, and each counter tracks the number of times that the corresponding target search term occurs at the corresponding Webpage.

4. The computer implemented method of claim 3, wherein generating the statistical data comprises generating a weighting factor for the target search term based, at least in part, on the number of times that the target search term occurs at the target Webpage and applying the weighting factor to each of the counters corresponding to the target search term.

5. The computer-implemented method of claim 1, wherein generating the statistical data comprises segmenting the plurality of Webpages into a plurality of clusters based, at least in part, on a similarity of the search terms applied to the Webpages.

6. The computer-implemented method of claim 5, wherein segmenting the plurality of Webpages comprises generating a graph comprising a node corresponding to a Webpage and an edge corresponding to a pair of Webpages at which a common search term occurs.

7. The computer-implemented method of claim 6, wherein segmenting the plurality of Webpages comprises generating a Jaccard index for the edge.

9. The computer-implemented method of claim 5, wherein generating the statistical data comprises ranking the Webpages in each cluster according to the number of searches performed at each of the Webpages.

18. The computer-implemented method of claim 1, wherein the target Webpage is associated with a target Website and the another Webpage is associated with another Website unaffiliated with the target Website.

8. A computer system, comprising:

a processor that is configured to execute machine-readable instructions; and

a memory device that stores Web activity data generated by a plurality of users at a plurality of Webpages associated with a plurality of unaffiliated Websites, wherein the Web activity data comprises a plurality of query Uniform Resource Locators (URLs) that represent searches performed by users; and

instructions that are executable by the processor, the instructions comprising;

a search term extractor configured to obtain a plurality of search terms from the plurality of query URLs and associate each of the search terms with a corresponding Webpage, wherein the search term extractor is a classifier, which is generated by a training system and configured to identify a portion of the query URLs that include the search terms; and

a data generator to generate statistical data from the Web activity data based, at least in part, on the search terms, wherein the statistical data comprises a first statistical data for a target search term associated with a target Website and a second statistical data for the target search term associated with another Website.

10. The computer-implemented method of claim 8, wherein the data generator is to:

search the Web activity data to identify Websites at which the target search term occurs.

11. The computer-implemented method of claim 10, wherein the data generator is to:

count the number of times that the target search term occurs at each of the Websites; and

rank the Websites based, at least in part, on the count.

12. The computer-implemented method of claim 10, wherein the data generator is configured to identify a competitive Website based, at least in part, on a similar search term occurring at the target Website and the competitive Website.

13. The computer-implemented method of claim 8, wherein the data generator is configured to group the Webpages into clusters based, at least in part, on a similarity of the search terms that occur at to the Webpages.

14. The computer-implemented method of claim 8, wherein the data generator is configured to rank the Webpages in each cluster according to the number of searches performed at each of the Webpages.

15. A tangible, non-transitory, computer-readable medium, comprising code configured to direct a processor to:

obtain a collection of Web activity data, wherein the Web activity data comprises a plurality of query Uniform Resource Locators (URLs) that represent searches performed by users;

obtain a plurality of search terms from the plurality of query Uniform Resource Locators (URLs), wherein the search terms are obtained by a classifier, which is generated by a training system and configurcd to identify a portion of the query URLs that include the search terms; and

generate statistical data from the Web activity data based, at least in part, on the search terms, wherein the statistical data corresponds to the online activity at a plurality of Webpages associated with a plurality of unaffiliated Websites and comprises a first statistical data for a target search term associated with a target Website and a second statistical data for the target search term associated with another Website.

16. The tangible, non-transitory, computer-readable medium of claim 15, comprising code to:

search the Web activity data to identify a competitive Website based, at least in part, on the number of times that the target search term occurs at the competitive Website.

17. The tangible, non-transitory, computer-readable medium of claim 15, comprising code to:

segment the plurality of Webpages into a plurality of clusters based, at least in part, on a similarity of the search terms applied to the Webpages; and

rank the Webpages in each cluster according to the number of searches performed at each of the Webpages.