Method and system for site path evaluation using web session clustering

• US 20060015504A1
• Filed: 07/15/2004
• Published: 01/19/2006
• Est. Priority Date: 07/15/2004
• Status: Active Grant

• Current Assignee: Hewlett Packard Enterprise Development LP (Hewlett-Packard Enterprise Company)
• Original Assignee: Hewlett-Packard Development Company, L.P. (HP Inc.)
• Inventors: Benson, Alan, Yu, Qingfeng, Lakshminarayan, Choudur K.
• Granted Patent: US 8,572,233 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/906 Clustering; Classification
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques

Abstract

A method and system for site path evaluation using web session clustering is provided. The method identifies properties of a plurality of web site traversal paths. Data is acquired from a plurality of sessions corresponding to at least a portion of the plurality of web page traversal paths. Portions of the web site traversal paths are grouped into a unified web page category. The plurality of sessions is clustered into a plurality of web session clusters according to a similarity measure. One of the plurality of web session clusters most closely exhibiting at least one predefined metric is selected for analysis of the propertied of a web page traversal path contained therein. A system includes a plurality of web pages, a monitoring program and a computational process configured to receive data and identify properties of the plurality of traversal paths.

Claims

1. A method for identifying properties of a plurality of web site traversal paths, comprising:

a. acquiring data from a plurality of sessions corresponding to at least a portion of said plurality of web page traversal paths;

b. grouping portions of at least one of said web site traversal paths into a unified web page category;

c. clustering said plurality of sessions according to a similarity measure into a plurality of web session clusters, at least one of said plurality of sessions comprises a unified web page category; and

d. selecting one of said plurality of web session clusters most closely exhibiting at least one predefined metric from said plurality of web session clusters for analysis of said properties of a web page traversal path contained therein.

2. The method of claim 1, wherein said acquiring data from a plurality of sessions, includes:

a. acquiring data from a plurality of user logs corresponding to at least a portion of said plurality of web page traversal paths during a corresponding plurality of sessions.

3. The method of claim 2, wherein said acquiring data, includes:

a. tagging each web page of said plurality of web page traversal paths;

b. for each of said plurality of sessions,

i. forwarding a URL to a monitoring program to store as said data;

ii. said monitoring program compiling a user log for each of said plurality of sessions;

iii. upon completion of one of said plurality of sessions, forwarding said user log for compiling into said data from said one of said plurality of sessions.

4. The method of claim 3, wherein said tagging includes embedding a Java script fragment into each of said plurality of web pages.

5. The method of claim 1, wherein each of said plurality of sessions comprises at least one URL uniquely identifying a specific one of said plurality of web pages.

6. The method of claim 1, wherein grouping portions of at least one of said plurality of web page traversal paths into a unified web page category further includes parsing URL text strings identifying specific ones of said plurality of web pages in said at least one of said web page traversal paths to identify said unified web page category.

7. The method of claim 1, wherein grouping portions of at least one of said web page traversal path into a unified web page category further includes correlating traffic frequency relationships between specific ones of said plurality of web pages in said at least one of said web page traversal paths to identify said unified web page category.

8. The method of claim 1, wherein clustering said plurality of sessions comprises:

a. defining a similarity measure between two sessions; and

b. partitioning said plurality of sessions into a plurality of web session clusters according to said similarity measure.

9. The method of claim 8, wherein said partitioning comprises clustering said plurality of sessions according to a pair wise sequence alignment algorithm.

10. The method of claim 1, wherein selecting one or more of said plurality of web session clusters, comprises:

a. extracting an open sequence as a representative sequence from each of said web session clusters; and

b. analyzing said properties of a web page traversal path of said open sequence of each of said web session clusters.

11. The method of claim 10, wherein said open sequence is identified according to an APRIORI algorithm.

12. A system for extracting properties from data, comprising:

a. a web site including a plurality of web pages;

b. a browser configured to request and receive ones of said plurality of web pages as requested;

c. a monitoring program configured to receive data identifying each of said ones of said plurality of web pages received by said browser during a session causing a web site traversal path through said plurality of web pages for a session and to forward said data; and

.

d. a computational process configured to receive said data and in conjunction with other data, identify said properties of said plurality of traversal paths.

13. The system of claim 12, wherein said plurality of web pages on said web site further include a tag uniquely identifying each of said plurality of web pages and wherein said tag allows said monitoring program to form said data identifying each of said ones of said plurality of web pages received by said browser.

14. The system of claim 13, wherein said tag comprises a Java script fragment configured to forward data identifying said one of said web page to said monitoring program.

15. The system of claim 12, wherein said computational process, comprises:

a. a page categorization process to group portions of at least one of said web page traversal path into a unified web page category;

b. a web session clustering process to cluster said plurality of data sessions according to a similarity measure into a plurality of web session clusters, at least one of said plurality of data sessions comprises a unified web page category; and

c. a session clustering evaluation process to select one or more of said plurality of web session clusters most closely exhibiting at least one metric of interest from said plurality of web session clusters for analysis of said properties of said portions of said plurality of web page-traversal path contained therein.

16. A computer-readable medium having computer-executable instructions for identifying properties of a plurality of web site traversal paths, said computer-executable instructions for:

a. acquiring data from a plurality of sessions corresponding to at least a portion of said plurality of web page traversal paths;

b. grouping portions of at least one of said web site traversal paths into a unified web page category;

c. clustering said plurality of sessions according to a similarity measure into a plurality of web session clusters, at least one of said plurality of sessions comprises a unified web page category; and

d. selecting one of said plurality of web session clusters most closely exhibiting at least one predefined metric from said plurality of web session clusters for analysis of said properties of a web page traversal path contained therein.

17. The computer-readable medium of claim 16 having computer-executable instructions, wherein said instruction for grouping portions of at least one of said web page traversal path, further includes computer-executable instructions for parsing URL text strings identifying specific ones of said web pages in said at least one of said web page traversal path to identify said unified web page category.

18. The computer-readable medium of claim 16 having computer-executable instructions, wherein said instruction for grouping portions of at least one of said web page traversal path, further includes computer-executable instructions for correlating traffic frequency relationships between specific ones of said web pages in said at least one of said web page traversal path to identify said unified web page category.

19. The computer-readable medium of claim 16 having computer-executable instructions, wherein said instruction for clustering said plurality of sessions, further includes computer-executable instructions for:

a. defining a similarity measure between two sessions; and

b. partitioning said plurality of sessions into a plurality of web session clusters according to said similarity measure.

20. The computer-readable medium of claim 16 having computer-executable instructions, wherein said instruction for selecting one or more of said plurality of web session clusters, further includes computer-executable instructions for:

a. extracting an open sequence as a representative sequence from each of said web session clusters; and

b. analyzing said properties of a web page traversal path of said open sequence of each of said web session clusters.

21. A system for identifying properties of a plurality of web site traversal paths, comprising:

a. means for acquiring data from a plurality of sessions corresponding to at least a portion of said plurality of web page traversal paths;

b. means for grouping portions of at least one of said web site traversal paths into a unified web page category;

c. means for clustering said plurality of sessions according to a similarity measure into a plurality of web session clusters, at least one of said plurality of sessions comprises a unified web page category; and

d. means for selecting one of said plurality of web session clusters most closely exhibiting at least one predefined metric from said plurality of web session clusters for analysis of said properties of a web page traversal path contained therein.