Multi-dimensional query expansion employing semantics and usage statistics
View Patent ↗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.
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.