Multi-Dimensional Query Expansion Employing Semantics and Usage Statistics

• US 20130254155A1
• Filed: 03/23/2012
• Published: 09/26/2013
• Est. Priority Date: 03/23/2012
• Status: Active Grant

• Current Assignee: SAP SE
• Original Assignee: SAP AG (SAP SE)
• Inventors: Thollot, Raphael, Kuchmann-Beauger, Nicolas, FollenFant, Corentin
• Granted Patent: US 8,762,324 B2
• Time in Patent Office: Days
• US Class Current: 707/602
• CPC Class Codes: G06F 16/24575 using context
• CPC Class Codes: G06F 16/248 Presentation of query results
• CPC Class Codes: G06F 16/283 Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP
• CPC Class Codes: G06F 16/3338 Query expansion
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 2216/03 Data mining

Abstract

Embodiments relate to systems and methods employing personalized query expansion to suggest measures and dimensions allowing iterative building of consistent queries over a data warehouse. Embodiments may leverage one or more of: semantics defined in multi-dimensional domain models, user profiles defining preferences, and collaborative usage statistics derived from existing repositories of Business Intelligence (BI) documents (e.g. dashboards, reports). Embodiments may utilize a collaborative co-occurrence value derived from profiles of users or social network information of a user.

Claims

1. A computer-implemented method comprising:

providing a query expansion system comprising an engine, in communication with a user profile and with a business intelligence platform comprising a data warehouse, a multi-dimensional domain model, and a repository of documents;

causing the query expansion system to receive a query posed by a user to the business intelligence platform, the query comprising a measure of the multi-dimensional domain model;

causing the engine to determine a co-occurrence value for the query in the data warehouse based upon the repository of documents and a preference in the user profile; and

causing the query expansion system to display to the user a query result based upon the co-occurrence value.

2. The method of claim 1 wherein determining the co-occurrence value comprises causing the engine to receive information from the document repository to determine a user-specific co-occurrence value.

3. The method of claim 1 wherein determining the co-occurrence value comprises causing a second engine of the query expansion system to receive social network information of the user from the user profile to determine a collaborative co-occurrence value.

4. The method of claim 3 wherein determining the collaborative co-occurrence value comprises applying a weight based upon a distance from the user in the social network information.

5. The method of claim 1 wherein:

the query result is presented to the user as a first link to a first dimension of the multi-dimensional domain model, together with a second link to a second query result based upon a second dimension of the multi-dimensional domain model; and

the user accesses the query result comprising a chart, by activating the first link.

6. The method of claim 1 wherein determining the co-occurrence value comprises defining a Jaccard index.

7. A non-transitory computer readable storage medium embodying a computer program for performing a method, said method comprising:

providing a query expansion system comprising an engine, in communication with a user profile and with a business intelligence platform comprising a data warehouse, a multi-dimensional domain model, and a repository of documents;

causing the query expansion system to receive a query posed by a user to the business intelligence platform, the query comprising a measure of the multi-dimensional domain model;

causing the engine to determine a co-occurrence value for the query in the data warehouse based upon the repository of documents and a preference in the user profile; and

causing the query expansion system to display to the user a query result based upon the co-occurrence value.

8. The non-transitory computer readable storage medium of claim 7 wherein determining the co-occurrence value comprises causing the engine to receive information from the document repository to determine a user-specific co-occurrence value.

9. The non-transitory computer readable storage medium of claim 8 wherein determining the co-occurrence value comprises causing a second engine of the query expansion system to receive social network information of the user from the user profile to determine a collaborative co-occurrence value.

10. The non-transitory computer readable storage medium of claim 9 wherein determining the collaborative co-occurrence value comprises applying a weight based upon a distance from the user in the social network information.

11. The non-transitory computer readable storage medium of claim 7 wherein:

the query result is presented to the user as a first link to a first dimension of the multi-dimensional domain model, together with a second link to a second query result based upon a second dimension of the multi-dimensional domain model; and

the user accesses the query result comprising a chart, by activating the first link.

12. The non-transitory computer readable storage medium of claim 7 wherein determining the co-occurrence value comprises defining a Jaccard index.

13. A computer system comprising:

one or more processors;

a software program, executable on said computer system, the software program configured to;

communicate with a query expansion system comprising an engine, in communication with a user profile and with a business intelligence platform comprising a data warehouse, a multi-dimensional domain model, and a repository of documents;

causing the query expansion system to receive a query posed by a user to the business intelligence platform, the query comprising a measure of the multi-dimensional domain model;

causing the engine to determine a co-occurrence value for the query in the data warehouse based upon the repository of documents and a preference in the user profile; and

causing the query expansion system to display to the user a query result based upon the co-occurrence value.

14. The computer system of claim 13 wherein determining the co-occurrence value comprises causing the engine to receive information from the document repository to determine a user-specific co-occurrence value.

15. The computer system of claim 13 wherein determining the co-occurrence value comprises causing a second engine of the query expansion system to receive social network information of the user from the user profile to determine a collaborative co-occurrence value

16. The computer system of claim 15 wherein determining the collaborative co-occurrence value comprises applying a weight based upon a distance from the user in the social network information.

17. The computer system of claim 13 wherein:

the query result is presented to the user as a first link to a first dimension of the multi-dimensional domain model, together with a second link to a second query result based upon a second dimension of the multi-dimensional domain model; and

the user accesses the query result comprising a chart, by activating the first link.

18. The computer system of claim 13 wherein determining the co-occurrence value comprises defining a Jaccard index.