System and method of managing data in a distributed computing environment

• US 10,423,590 B2
• Filed: 06/23/2017
• Issued: 09/24/2019
• Est. Priority Date: 06/23/2017
• Status: Active Grant

• Current Assignee: Bank of America Corp.
• Original Assignee: Bank of America Corp.
• Inventors: Tigli, A. Ugur, McCormack, James, Beye, Rick A., Kapur, Monika, Shushkovsky, Konstantin
• Primary Examiner(s): Meky, Moustafa M
• Application Number:
  US15/631,854
• Publication Number:
  US 20180373739A1
• Time in Patent Office: 823 Days
• CPC Class Codes: G06F 16/178 Techniques for file synchronisation in file systems
• CPC Class Codes: G06F 16/211 Schema design and management
• CPC Class Codes: G06F 16/25 Integrating or interfacing systems involving database management systems
• 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: H04L 67/10 in which an application is distributed across nodes in the network software deployment G06F8/60; multiprogramming arrangements G06F9/46

Abstract

In one or more embodiments, one or more systems, processes, and/or methods may receive a first data stream and determine a pattern from the first data stream. At least one rule set based at least on the pattern may be determined. A second data stream, different from the first data stream may be received and entities may be determined, where each of the entities may be associated with respective data of the second data stream that satisfies the at least one rule set. At least one data object of the second data stream may be tagged, in response to determining the entities. In one or more embodiments, tagging the at least one data object may associate the at least one data object with at least one of the entities.

Claims

1. A system, comprising:

a processor; and

a memory medium communicatively coupled to the processor;

wherein the memory medium stores instructions that are executable by the processor and that when executed by the processor, cause the system to;

receive a first data stream;

determine a pattern from the first data stream;

determine at least one rule set based at least on the pattern;

receive a second data stream, different from the first data stream;

determine a plurality of entities, each of the plurality of entities associated with respective data of the second data stream that satisfies the at least one rule set; and

in response to determining the plurality of entities, tag at least one data object of the second data stream.

2. The system of claim 1, wherein the memory medium further stores instructions that are executable by the processor and that when executed by the processor, cause the system to further:

in response to determining the plurality of entities, provide an alert.

3. The system of claim 1,

wherein the at least one data object includes information associated with at least one action;

and

wherein the at least one action that satisfies the at least one rule set.

4. The system of claim 3, wherein the at least one action indicates at least one money laundering action.

5. The system of claim 1, wherein to tag the at least one data object of the second data stream associates the at least one data object with at least one of the plurality of entities.

6. The system of claim 1, wherein to tag the at least one data object, the memory medium further stores instructions that are executable by the processor and that when executed by the processor, cause the system to further flag the at least one data object with a legal hold.

7. The system of claim 1, wherein the memory medium further stores instructions that are executable by the processor and that when executed by the processor, cause the system to further:

store a first plurality of data objects, that includes the at least one data object, via a database graph;

receive a query that indicates a selection of data elements and a confidence interval;

search the database graph based at least on the selection of data elements and the confidence interval; and

respond to the query with a second plurality of data objects that are associated with the selection of data elements and the confidence interval.

8. A method, comprising:

receiving a first data stream;

determining a pattern from the first data stream;

determining at least one rule set based at least on the pattern;

receiving a second data stream, different from the first data stream;

determining a plurality of entities, each of the plurality of entities associated with respective data of the second data stream that satisfies the at least one rule set; and

in response to the determining the plurality of entities, tagging at least one data object of the second data stream.

9. The method of claim 8, further comprising:

in response to the determining the plurality of entities, providing an alert.

10. The method of claim 8,

wherein the at least one data object includes information associated with at least one action;

and

wherein the at least one action that satisfies the at least one rule set.

11. The method of claim 10, wherein the at least one action indicates at least one money laundering action.

12. The method of claim 8, wherein the tagging the at least one data object associates the at least one data object with at least one of the plurality of entities.

13. The method of claim 8, wherein the tagging the at least one data object includes flagging the at least one data object with a legal hold.

14. The method of claim 8, further comprising:

storing a first plurality of data objects, that includes the at least one data object, via a database graph;

receiving a query that indicates a selection of data elements and a confidence interval;

searching the database graph based at least on the selection of data elements and the confidence interval; and

responding to the query with a second plurality of data objects that are associated with the selection of data elements and the confidence interval.

15. A computer-readable non-transient memory medium that comprises instructions executable by a processor of a system, wherein when the processor executes the instructions, the instruction cause the system to:

receive a first data stream;

determine a pattern from the first data stream;

determine at least one rule set based at least on the pattern;

receive a second data stream, different from the first data stream;

determine a plurality of entities, each of the plurality of entities associated with respective data of the second data stream that satisfies the at least one rule set; and

in response to determining the plurality of entities, tag at least one data object of the second data stream.

16. The computer-readable non-transient memory medium of claim 15, wherein the memory medium further stores instructions that are executable by the processor and that when executed by the processor, cause the system to further:

in response to determining the plurality of entities, provide an alert.

17. The computer-readable non-transient memory medium of claim 15, wherein the at least one data object includes information associated with at least one action;

and

wherein the at least one action that satisfies the at least one rule set.

18. The computer-readable non-transient memory medium of claim 17, wherein the at least one action indicates at least one money laundering action.

19. The computer-readable non-transient memory medium of claim 15, wherein to tag the at least one data object, the memory medium further stores instructions that are executable by the processor and that when executed by the processor, cause the system to further flag the at least one data object with a legal hold.

20. The computer-readable non-transient memory medium of claim 15, wherein the memory medium further stores instructions that are executable by the processor and that when executed by the processor, cause the system to further:

store a first plurality of data objects, that includes the at least one data object, via a database graph;

receive a query that indicates a selection of data elements and a confidence interval;

search the database graph based at least on the selection of data elements and the confidence interval; and

respond to the query with a second plurality of data objects that are associated with the selection of data elements and the confidence interval.