ENTERPRISE DATA PROCESSING

• US 20140059056A1
• Filed: 08/20/2013
• Published: 02/27/2014
• Est. Priority Date: 08/22/2012
• Status: Active Grant

• Current Assignee: Bitvore Corporation
• Original Assignee: Bitvore Corporation
• Inventors: Chaney, Alan, Cover, Clay, Bolcer, Greg
• Granted Patent: US 9,547,682 B2
• Time in Patent Office: Days
• US Class Current: 707/749
• CPC Class Codes: G06F 16/23 Updating
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/256 in federated or virtual databases
• CPC Class Codes: G06F 16/27 Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
• CPC Class Codes: G06F 16/31 Indexing; Data structures therefor; Storage structures
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation

Abstract

An enterprise data processing module and method are described herein. The enterprise data processing module comprises at least one collector and at least one analyzer. The collectors may be operable to collect data pieces from a plurality of data sources. The analyzers may be operable to analyze the collected data pieces to determine cross-source relationships that exist between the data pieces collected from the plurality of sources. The analyzed data pieces may be stored in one or more big-data databases as blocks of data according to the cross-source relationships.

Claims

1. An enterprise data processing module, comprising:

a collector operable to collect data pieces from a plurality of data sources; and

an analyzer operable to analyze the collected data pieces to determine cross-source relationships that exist between the data pieces collected from the plurality of sources, wherein the analyzed data pieces are stored in one or more big-data databases as blocks of data according to the cross-source relationships.

2. The enterprise data processing module of claim 1, wherein the enterprise data processing module comprises:

a user interface operable to receive a request from a user to interact with a data group stored in the one or more big-data databases, wherein the request is attempting to utilize information from the cross-source relationship.

3. The enterprise data processing module of claim 2, wherein information from the cross-source relationship comprises conclusion data that supports a schema.

4. The enterprise data processing module of claim 3, wherein if the user has permission to access information from the cross-source relationship, the request is processed to return the conclusion data without extracting all underlying data required to compute the requested conclusion data.

5. The enterprise data processing module of claim 1, wherein the cross-source relationship comprises a degree of correlation that is determined by a correlation intensity algorithm.

6. The enterprise data processing module of claim 5, wherein the correlation intensity algorithm determines a level of similarity with respect to the number of unique concepts in each data piece.

7. The enterprise data processing module of claim 5, wherein the correlation intensity algorithm determines a level of similarity with respect to a complexity of the data pieces.

8. The enterprise data processing module of claim 5, wherein the correlation intensity algorithm determines a level of similarity with respect to a size of the data pieces.

9. The enterprise data processing module of claim 5, wherein the correlation intensity algorithm determines a level of similarity with respect to a spam score of each data piece.

10. The enterprise data processing module of claim 5, wherein the correlation intensity algorithm determines a level of similarity with respect to a readability score of each data piece.

11. A method for enterprise data processing, the method comprising:

collecting data pieces from a plurality of data sources;

determining a cross-source relationship that exist between data pieces collected from different sources of the plurality of sources;

creating one or more data globs, each data glob including the data pieces, the cross-source relationship and one or more access rules; and

storing the one or more data globs in one or more big-data databases.

12. The method of claim 11, wherein the method comprises:

receiving a request from a user to interact with a data glob stored in the one or more big-data databases, wherein the request is attempting to utilize the cross-source relationship to request conclusion data that is based on the data pieces and the cross-source relationship.

13. The method of claim 12, wherein information from the cross-source relationship comprises conclusion data that supports a schema.

14. The method of claim 12, wherein if the user has permission to access information from the cross-source relationship, the method comprises processing the request to return the conclusion data without extracting all underlying data required to compute the requested conclusion data.

15. The method of claim 11, wherein determining a cross-source relationship comprises determining a degree of correlation according to a correlation intensity algorithm.

16. The method of claim 15, wherein the correlation intensity algorithm determines a level of similarity with respect to the number of unique concepts in each data piece.

17. The enterprise data processing module of claim 15, wherein the correlation intensity algorithm determines a level of similarity with respect to a complexity of the data pieces.

18. The enterprise data processing module of claim 15, wherein the correlation intensity algorithm determines a level of similarity with respect to a size of the data pieces.

19. The enterprise data processing module of claim 15, wherein the correlation intensity algorithm determines a level of similarity with respect to a spam score of each data piece.

20. The enterprise data processing module of claim 15, wherein the correlation intensity algorithm determines a level of similarity with respect to a readability score of each data piece.