Multi-dimensional query expansion employing semantics and usage statistics

• US 9,690,848 B2
• Filed: 06/05/2014
• Issued: 06/27/2017
• Est. Priority Date: 03/23/2012
• Status: Active Grant

• Current Assignee: SAP SE
• Original Assignee: SAP SE
• Inventors: Kuchmann-Beauger, Nicolas, Thollot, Raphael, FollenFant, Corentin
• Primary Examiner(s): Burke, Jeffrey A
• Assistant Examiner(s): VU, THONG H
• Application Number:
  US14/297,259
• Publication Number:
  US 20140337309A1
• Time in Patent Office: 1,118 Days
• Field of Search: 707 3, 707 5, 707602, 707739, 707750, 707639, 707769, 707710, 707776, 705 5, 705 261
• 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 a first engine, a second engine, and a data warehouse;

causing the query expansion system to receive a query posed by a user to the data warehouse;

causing the first engine to determine a user specific co-occurrence value for the query in the data warehouse;

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

causing the second engine to determine a collaborative co-occurrence value for the query in the data warehouse based upon social/trust network information taking into account a relationship of the user to another entity in a social/trust network, wherein the relationship of the user to the other entity in the social/trust network is determined from a user profile, and wherein determining the collaborative co-occurrence value comprises applying a weight based upon a distance of the another entity from the user in the social/trust network information; and

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

2. The method of claim 1 wherein the social/trust network information comprises information from a social network.

3. The method of claim 1 wherein the social/trust network information comprises information from an enterprise directory.

4. The method of claim 1 wherein the social/trust network information comprises a business contact.

5. The method of claim 1 wherein the user specific co-occurrence value is further determined from a repository of documents and a user preference.

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 a first engine, a second engine, and a data warehouse;

causing the query expansion system to receive a query posed by a user to the data warehouse;

causing the first engine to determine a user specific co-occurrence value for the query in the data warehouse;

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

causing the second engine to determine a collaboration co-occurrence value for the query in the data warehouse based upon social/trust network information taking into account a relationship of the user to another entity in a social/trust network, wherein the relationship of the user to the other entity in the social/trust network is determined from a user profile, and wherein determining the collaboration co-occurrence value comprises applying a weight based upon a distance of the another entity from the user in the social/trust network information; and

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

8. The non-transitory computer readable storage medium of claim 7 wherein the social/trust network information comprises information from a social network.

9. The non-transitory computer readable storage medium of claim 7 wherein the social/trust network information comprises information from an enterprise directory.

10. The non-transitory computer readable storage medium of claim 7 wherein the social/trust network information comprises a business contact.

11. The non-transitory computer readable storage medium of claim 7 wherein the user specific co-occurrence value is further determined from a repository of documents and a user preference.

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;

provide a query expansion system comprising a first engine, a second engine, and a data warehouse;

cause the query expansion system to receive a query posed by a user to the data warehouse;

cause the first engine to determine a user specific co-occurrence value for the query in the data warehouse;

cause the query expansion system to display to the user a first query result based upon the user specific co-occurrence value;

cause the second engine to determine a collaboration co-occurrence value for the query in the data warehouse based upon social/trust network information taking into account a relationship of the user to another entity in a social/trust network, wherein the relationship of the user to the other entity in the social/trust network is determined from a user profile, and wherein determining the collaboration co-occurrence value comprises applying a weight based upon a distance of the another entity from the user in the social/trust network information; and

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

14. The computer system of claim 13 wherein the social/trust network information comprises information from a social network.

15. The computer system of claim 13 wherein the social/trust network information comprises information from an enterprise directory.

16. The computer system of claim 13 wherein the social/trust network information comprises a business contact.

17. The computer system of claim 13 wherein the user specific co-occurrence value is further determined from a repository of documents and a user preference.