Enterprise data processing

• US 10,430,480 B2
• Filed: 12/06/2016
• Issued: 10/01/2019
• Est. Priority Date: 08/22/2012
• Status: Active Grant

• Current Assignee: Bitvore Corporation
• Original Assignee: Bitvore Corporation
• Inventors: Bolcer, Greg, Chaney, Alan, Cover, Clay
• Primary Examiner(s): Corrielus, Jean M
• Application Number:
  US15/370,717
• Publication Number:
  US 20170300578A1
• Time in Patent Office: 1,029 Days
• Field of Search: 707749, 707736
• 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. A system, the system comprising:

a non-transitory computer readable medium storing a sequence of instructions; and

a processor operable to execute the sequence of instructions to;

collect a plurality of data pieces from a plurality of data sources,

determine a cross-source relationship between the plurality of data pieces,

transferring the data pieces to a plurality of databases as blocks of data according to the cross-source relationship, wherein the cross-source relationship comprises a plurality of metrics, each metric associated with a different data characteristic, and wherein the plurality of databases are located in one or more data centers, and wherein the one or more data centers are maintained by a third party and are available through one or more network connections, and

generating a weighted combination of the plurality of metrics of the cross-source relationship.

2. The system of claim 1, wherein the system comprises:

a user interface operable to receive a request from a user to assign one or more weights to the plurality of metrics of the cross-source relationship.

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

4. The system 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 system of claim 1, wherein the cross-source relationship comprises a degree of correlation that is determined by a correlation intensity algorithm.

6. The system 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 system of claim 5, wherein the correlation intensity algorithm determines a level of similarity with respect to a complexity of the data pieces.

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

9. The system 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 system 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 operating a system, the system comprising a memory and an analysis engine, the method comprising:

collecting data pieces, by a pre-processor, into the memory from a plurality of data sources;

determining, by a processor, a cross-source relationship that exists between data pieces collected from different sources of the plurality of sources, the determining being performed by the analysis engine, wherein the cross-source relationship comprises a plurality of metrics, each metric corresponding to a different data characteristic;

assigning, by the processor, one or more weights to each metric of the plurality of metrics of the cross-source relationship, the assigning being performed by the analysis engine;

generating, by the processor, conclusion data that is based on the request from the user, the data pieces and the cross-source relationship, the conclusion data being a single composite value that combines the one or more weights from each metric of the plurality of metrics of the cross-source relationship, the generating being performed by the analysis engine;

generating, by the processor, one or more data globs, each data glob including the data pieces, the cross-source relationship and one or more access rules, the generating being performed by the analysis engine; and

storing, by the processor, the one or more data globs in a plurality of databases located in one or more data centers, wherein the one or more data centers are maintained by a third party and are available through one or more network connections.

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

receiving a request from a user to interact with a data glob stored in one or more databases of the plurality of databases.

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 method of claim 15, wherein the correlation intensity algorithm determines a level of similarity with respect to a complexity of the data pieces.

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

19. The method 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 method of claim 15, wherein the correlation intensity algorithm determines a level of similarity with respect to a readability score of each data piece.