Cross-model filtering

• US 9,720,972 B2
• Filed: 06/17/2013
• Issued: 08/01/2017
• Est. Priority Date: 06/17/2013
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Inventors: Faghihi Rezaei, Pedram, Baumgartner, Patrick J., Netz, Amir M.
• Primary Examiner(s): Cao, Phuong Thao
• Application Number:
  US13/919,857
• Publication Number:
  US 20140372481A1
• Time in Patent Office: 1,506 Days
• Field of Search: 707775
• CPC Class Codes: G06F 16/215 Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
• CPC Class Codes: G06F 16/24553 of query operations
• CPC Class Codes: G06F 16/24564 Applying rules; Deductive queries
• CPC Class Codes: G06Q 30/06 Buying, selling or leasing transactions

Abstract

Presenting data from different data providers in a correlated fashion. The method includes performing a first query on a first data set controlled by a first entity to capture a first set of data results. The method further includes performing a second query on a second data set controlled by a second entity to capture a second set of data results. The method includes receiving a selection of one or more results from the first data set. The method further includes using the one or more selected results, consulting a relationship ontology that correlates data stored in different data stores controlled by different entities, to identify one or more relationships between data in the selected results set and the second data set.

Claims

1. A method of constructing data from different data providers in a correlated fashion, the method being performed by one or more processors of a computer system, the method comprising:

performing a first query on a first data set controlled by a first entity to capture a first set of data results;

performing a second query on a second data set controlled by a second entity to capture a second set of data results, wherein the second query is generated independently from the first query such that the second query is distinct from, and not dependent on, the first query;

receiving a selection of one or more results from the first set of data results;

using the selection from the first set of data results to consult a relationship ontology that correlates data stored in different data stores controlled by different entities and further to identify one or more relationships between data in the selection from the first set of data results and the second set of data results subsequent to performing both the first query and the second query, wherein the identified one or more relationships between the data in the selection from the first set of data results and the second set of data results are stored in the relationship ontology, and wherein at least one of the identified one or more relationships stored in the relationship ontology is manually defined;

constructing a new query over the second data set based on identifying the one or more relationships between the data in the selection from the first set of data results and the second set of data results; and

performing the new query on the second data set such that results from the new query are correlated with the selection from the first set of data results.

2. The method of claim 1 further comprising:

performing additional operations on the second set of data results based on the identified one or more relationships between the data in the selection from the first set of data results and the second set of data results.

3. The method of claim 2, wherein performing the additional operations on the second set of data results comprises highlighting an element in the second set of data results.

4. The method of claim 2, wherein performing the additional operations on the second set of data results comprises analyzing results in the second set of data results.

5. The method of claim 2, wherein performing the additional operations on the second set of data results comprises categorizing results in the second set of data results.

6. The method of claim 2, wherein performing the additional operations on the second set of data results comprises sorting results in the second set of data results.

7. The method of claim 2, wherein performing the additional operations on the second set of data results comprises filtering results in the second set of data results.

8. The method of claim 1, further comprising:

displaying a correlation between the first set of data results and the second set of data results.

9. The method of claim 1, wherein the second query is performed prior to receiving the selection of the one or more results from the first set of data results.

10. The method of claim 1, wherein the second set of data results is displayed in a bar chart.

11. A system for constructing data from different data providers in a correlated fashion, the system comprising:

one or more processors; and

one or more computer readable hardware storage devices having stored thereon computer executable instructions that are executable by at least one of the one or more processors to cause the system to;

perform a first query on a first data set controlled by a first entity to capture a first set of data results;

perform a second query on a second data set controlled by a second entity to capture a second set of data results, wherein the second query is generated independently from the first query such that the second query is distinct from, and not dependent on, the first query;

receive a selection of one or more results from the first set of data results;

use the selection from the first set of data results to consult a relationship ontology that correlates data stored in different data stores controlled by different entities and further to identify one or more relationships between data in the selection from the first set of data results and the second set of data results subsequent to performing both the first query and the second query, wherein the identified one or more relationships between the data in the selection from the first set of data results and the second set of data results are stored in the relationship ontology, and wherein at least one of the identified one or more relationships stored in the relationship ontology is manually defined;

construct a new query over the second data set based on identifying the one or more relationships between the data in the selection from the first set of data results and the second set of data results; and

perform the new query on the second data set such that results from the new query are correlated with the selection from the first set of data results.

12. The system of claim 11, wherein the computer executable instructions further cause the system to perform additional operations on the second set of data results based on the identified one or more relationships between the data in the selection from the first set of data results and the second set of data results.

13. The system of claim 12, wherein performing the additional operations on the second set of data results comprises highlighting an element in the second set of data results.

14. The system of claim 12, wherein performing the additional operations on the second set of data results comprises categorizing results in the second set of data results.

15. The system of claim 12, wherein performing the additional operations on the second set of data results comprises sorting results in the second set of data results.

16. The system of claim 12, wherein performing the additional operations on the second set of data results comprises filtering results in the second set of data results.

17. The system of claim 11, wherein the computer executable instructions further cause the system to display a correlation between the first set of data results and the second set of data results.

18. The system of claim 11, wherein the first data set is stored in a first table that includes column labels and the second data set is stored in a second table that also includes column labels, and wherein the computer executable instructions further cause the system to:

identify a first column of information in the first table, the first column including substantially similar information as a second column of information in the second table;

identify a first column label corresponding to the first column, the first column label being named differently than a second column label corresponding to the second column; and

in the relationship ontology, correlate the first column of information in the first table with the second column of information in the second table even though the first column label is named differently than the second column label.

19. The system of claim 18, wherein the first column label is expressed using a first language while the second column label is expressed using a second language.

20. A physical computer readable hardware storage device comprising computer executable instructions that are executable by one or more processors to cause the one or more processors to:

perform a plurality of queries on a plurality of data sets controlled by different entities, wherein a second query is generated independently from a first query such that the second query is distinct from, and not dependent on, the first query;

identify a result from one of the plurality of queries;

use a relationship ontology that correlates data stored in different data stores controlled by different entities to identify one or more relationships between data in the identified result and results from other queries in the plurality of queries subsequent to performing both the first query and the second query, wherein the identified one or more relationships between the data in the identified result and the results from the other queries in the plurality of queries are stored in the relationship ontology, and wherein at least one of the identified one or more relationships stored in the relationship ontology is manually defined;

construct a new query over the plurality of data sets based on identifying the one or more relationships between the data in the identified result and results from other queries; and

perform the new query such that results from the new query are correlated with the identified result.

21. The physical computer readable hardware storage device of claim 20, wherein identifying the result from the one of the plurality of queries comprises receiving a user selection.