Olap-based web access analysis method and system

• US 20040093412A1
• Filed: 10/29/2003
• Published: 05/13/2004
• Est. Priority Date: 05/18/2001
• Status: Active Grant

• Current Assignee: Hewlett Packard Enterprise Development LP (Hewlett-Packard Enterprise Company)
• Original Assignee: Meichun Hsu, Qiming Chen, Umeshwar Dayal
• Inventors: Hsu, Meichun, Chen, Qiming, Dayal, Umeshwar
• Granted Patent: US 7,171,406 B2
• Time in Patent Office: Days
• US Class Current: 709/224
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: Y10S 707/99932 Access augmentation or optimizing
• CPC Class Codes: Y10S 707/99933 Query processing, i.e. searching
• CPC Class Codes: Y10S 707/99934 Query formulation, input preparation, or translation
• CPC Class Codes: Y10S 707/99936 Pattern matching access

Abstract

A method and system for analyzing web access. First, a plurality of web log records are received. Next, multi-dimensional summary information is generated based on the web log records. Then, derivation and analysis are performed to discover usage patterns and rules for supporting business intelligence by using the multi-dimensional summary information.

Claims

1. A method of aggregating data comprising the steps of:

a) receiving a plurality of web log records; and

b) generating a volume cube having a plurality of dimensions based on the plurality of web log records; and

c) generating a first summary cube at a first level of abstraction based on the volume cube;

wherein the first summary cube has a plurality of cells and at least one parent dimension for each dimension of the volume cube;

wherein each cell includes a value that represents a summarization of corresponding cell values of the volume cube aggregated over all the dimensions of the volume cube.

2. The method of claim 1 further comprising:

d) generating a second summary cube at a second level of abstraction based on the first summary cube;

wherein the second summary cube has at least one parent dimension for each dimension of the first summary cube;

wherein the second summary cube is generated by limiting the dimensions of the first summary cube to a range of values above a predetermined value.

3. The method of claim 1 further comprising:

d) selectively generating a partial aggregate along one or more dimensions by using a query that specifies at least one dimension along which the partial aggregate is to be calculated.

4. The method of claim 3 wherein the first summary cube includes aggregated values;

and wherein the first summary cube includes a drill-down enabling mechanism for specifying relationships between dimensions of the volume cube and the dimensions of the first summary cube for enabling drill-down analysis.

5. The method of claim 1 wherein the first summary cube includes a plurality of dimensions;

and wherein each dimension corresponds to at least one dimensions of the volume cube.

6. The method of claim 5 wherein the first summary cube includes

a first dimension for specifying an origin;

a second dimension for specifying a subject; and

a third dimension for specifying a referring site.

7. The method of claim 2 wherein the second summary cube includes a plurality of dimensions;

and wherein each dimension corresponds to at least one dimensions of the first summary cube.

8. The method of claim 7 wherein the second summary cube includes

a first dimension for specifying a top origin;

a second dimension for specifying a top subject; and

a third dimension for specifying a top referring site.

9. The method of claim 1 wherein each web log record includes a plurality of fields.

10. The method of claim 9 wherein each web log record includes

a first field for storing an IP address of an origin site;

a second field for storing a target URI;

a third field for storing a referring site; and

a fourth field for storing a time.

11. A method for analyzing web access comprising the steps of:

a) receiving a plurality of web log records; and

b) generating multi-dimensional summary information based on the plurality of web log records; and

c) performing derivation and analysis to discover usage patterns and rules for supporting business intelligence by using the multi-dimensional summary information.

12. The method of claim 11 wherein the step of performing derivation and analysis to discover usage patterns and rules for supporting business intelligence includes one of usage analysis, web site traffic analysis, and business rules discovery.

13. The method of claim 11 further comprising the steps of:

d) performing multilevel and multidimensional analysis on the summary information to generate one of multidimensional patterns, multilevel patterns, and probability based patterns.

14. The method of claim 11 further comprising the steps of:

d) generating multilevel and multidimensional feature ranking cubes for ranking web access along multiple dimensions and at multiple levels.

15. The method of claim 14 wherein the step of generating multilevel and multidimensional feature ranking cubes for ranking web access along multiple dimensions and at multiple levels includes:

generating a first cube for ranked list of elements of a particular dimension, where a feature is represented by a dimension; and

generating a second cube for one of volume and probability distribution corresponding to the ranked list of elements of a particular dimension.

16. The method of claim 11 further comprising the steps of:

d) performing correlation analysis on the summary information to generate association rules for use in web access analysis.

17. The method of claim 16 wherein the step of performing correlation analysis on the summary information to generate association rules for use in web access analysis includes generating one of multilevel association rules with flexible base and dimensions and time-variant association rules.

18. A system for analyzing web access comprising:

a) a source of web log records; and

b) an OLAP engine when executing a web access analysis program for receiving a plurality of web log records, generating multidimensional summary information based on the web log records;

and performing derivation and analysis to discover usage patterns and rules for supporting business intelligence by using the multi-dimensional summary information.

19. The system of claim 18 wherein the web access analysis program includes

a feature ranking facility for generating multilevel and multidimensional feature ranking cubes for ranking web access along multiple dimensions and at multiple levels;

wherein the feature ranking facility generates a first cube for ranked list of elements of a particular dimension, where a feature is represented by a dimension; and

generates a second cube for one of volume and probability distribution corresponding to the ranked list of elements of a particular dimension.

20. The system of claim 18 wherein the web access analysis program includes

a correlation analysis facility for performing correlation analysis on the summary information to generate association rules for use in web access analysis;

wherein the correlation analysis facility generates one of multilevel association rules with flexible base and dimensions and time-variant association rules.

21. The system of claim 18 wherein the web access analysis program includes

a direct binning facility for concurrently generating a volume cube based on the plurality of web log records and directly generating a high diagonal cube based on plurality of web log records.