Intelligent database control systems with automated request assessments

• US 10,565,214 B2
• Filed: 03/22/2017
• Issued: 02/18/2020
• Est. Priority Date: 03/22/2017
• Status: Active Grant

• Current Assignee: Bank of America Corp.
• Original Assignee: Bank of America Corp.
• Inventors: Pidaparthi, Sita Maha Lakshmi, Ramasamy, Suki
• Primary Examiner(s): Dang, Thanh-Ha
• Application Number:
  US15/465,864
• Publication Number:
  US 20180276218A1
• Time in Patent Office: 1,063 Days
• Field of Search: 707723
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/258 Data format conversion from or to a database
• CPC Class Codes: G06F 21/552 involving long-term monitoring or reporting
• CPC Class Codes: G06F 21/6227 where protection concerns the structure of data, e.g. records, types, queries
• CPC Class Codes: G06F 2221/2105 Dual mode as a secondary aspect
• CPC Class Codes: G06N 20/00 Machine learning

Abstract

Aspects of the disclosure relate to intelligent database control systems for performing automated request assessments. In one embodiment, a computing device may receive, via a communication interface, a database request from a user computing device. The computing device may generate a legitimacy score associated with the database request based on one or more predetermined definitions. If the legitimacy score is above a predetermined threshold, the computing device may format the database request based on one or more of the predetermined definitions and the legitimacy score and command, via the communication interface, one or more databases to execute the database request. The computing device may format result set generated from the executed database request and transmit, via the communication interface, the formatted result set to the user computing device.

Claims

1. An intelligent database control computing platform, comprising:

at least one processor;

a communication interface communicatively coupled to the at least one processor; and

memory storing computer-readable instructions that, when executed by the at least one processor, cause the intelligent database control computing platform to;

receive a plurality of predetermined definitions including at least permission definitions, security role definitions, optimization level definitions;

receive, via the communication interface, from a user computing device, a database request, the database request including a format for the database request;

decode the database request into a plurality of objects corresponding to information included in the database request;

compare each object of the plurality of objects to a corresponding predetermined definition of the plurality of predetermined definitions;

based on the comparing, determine compliance of the database request with the plurality of predetermined definitions;

generate, based on the determined compliance of the database request with the plurality of predetermined definitions, a score associated with the database request;

determine whether the generated score is above a predetermined threshold;

responsive to determining that the score is above the predetermined threshold, format, using machine learning, the database request based on one or more of the predetermined definitions, the score, and the received format for the database request;

command, via the communication interface, one or more databases to execute the database request;

format a result set generated from the executed database request; and

transmit, via the communication interface, to the user computing device, the formatted result set.

2. The intelligent database control computing platform of claim 1, wherein the score is generated using machine learning algorithms.

3. The intelligent database control computing platform of claim 2, wherein the computer-readable instructions, when executed by the at least one processor, further cause the intelligent database control computing platform to:

receive, via the communication interface, from the one or more databases, execution details and logs corresponding to the executed database request;

analyze the execution details and logs based on one or more of Pareto analysis, causal analysis, and failure mode and effect analysis; and

update the machine learning algorithms used to generate the score based on the analysis of the execution details and logs.

4. The intelligent database control computing platform of claim 1, wherein the computer-readable instructions, when executed by the at least one processor, further cause the intelligent database control computing platform to:

responsive to determining that the legitimacy score is below the predetermined threshold, transmit, via the communication interface, to a database administrator computing device, an alert regarding the database request.

5. The intelligent database control computing platform of claim 1, wherein the one or more databases are one of a relational database and a NoSQL database.

6. The intelligent database control computing platform of claim 1, instructions that, when executed, cause the intelligent database control computing platform to:

receive resource information from one or more databases including expected processing time and available processing capacity;

determine that the resource information from the one or more databases indicates resources above a resource threshold level; and

delay executing the database request until the resources are below the resource threshold level.

7. A method, comprising:

at a computing platform comprising at least one processor, memory, and a communication interface;

receiving, by the at least one processor, a plurality of predetermined definitions including at least permission definitions, security role definitions, optimization level definitions;

receiving, via the communication interface, from a user computing device, a database request, the database request including a format for the database request;

decoding, by the at least one processor, the database request into a plurality of objects corresponding to information included in the database request;

comparing, by the at least one processor, each object of the plurality of objects to a corresponding predetermined definition of the plurality of predetermined definitions;

based on the comparing, determining, by the at least one processor, compliance of the database request with the plurality of predetermined definitions;

generating, by the at least one processor and based on the determined compliance of the database request with the plurality of predetermined definitions, a score associated with the database request based;

determining, by the at least one processor, whether the generated score is above a predetermined threshold;

responsive to determining that the score is above the predetermined threshold, formatting, using machine learning, the database request based on one or more of the predetermined definitions, the score, and the received format for the database request;

commanding, via the communication interface, one or more databases to execute the database request;

formatting a result set generated from the executed database request; and

transmitting, via the communication interface, to the user computing device, the formatted result set.

8. The method of claim 7, wherein the score is generated using machine learning algorithms.

9. The method of claim 8, further comprising:

receive, via the communication interface, from the one or more databases, execution details and logs corresponding to the executed database request;

analyze the execution details and logs based on one or more of Pareto analysis, causal analysis, and failure mode and effect analysis; and

update the machine learning algorithms used to generate the score based on the analysis of the execution details and logs.

10. The method of claim 7, further comprising:

responsive to determining that the score is below the predetermined threshold, transmitting, via the communication interface, to a database administrator computing device, an alert regarding the database request.

11. The method of claim 10, wherein the one or more databases are one of a relational database and a NoSQL.

12. One or more non-transitory computer-readable media storing instructions that, when executed by a computing device comprising at least one processor, memory, and a communication interface, cause the computing device to:

receive a plurality of predetermined definitions including at least permission definitions, security role definitions, optimization level definitions;

receive, via the communication interface, from a user computing device, a database request, the database request including a format for the database request;

decode the database request into a plurality of objects corresponding to information included in the database request;

compare each object of the plurality of objects to a corresponding predetermined definition of the plurality of predetermined definitions;

based on the comparing, determine compliance of the database request with the plurality of predetermined definitions;

generate, based on the determined compliance of the database request with the plurality of predetermined definitions, a score associated with the database request;

determine whether the generated score is above a predetermined threshold;

responsive to determining that the score is above the predetermined threshold, format, using machine learning, the database request based on one or more of the predetermined definitions, the score, and the received format for the database request;

command, via the communication interface, one or more databases to execute the database request;

format result set generated from the executed database request; and

transmit, via the communication interface, to the user computing device, the formatted result set.

13. The one or more non-transitory computer-readable media of claim 12, wherein the score is generated using machine learning algorithms.

14. The one or more non-transitory computer-readable media of claim 13, storing additional instructions that, when executed by the computing device, cause the computing device to:

receive, via the communication interface, from the one or more databases, execution details and logs corresponding to the executed database request;

analyze the execution details and logs based on one or more of Parato analysis, casual analysis, and failure mode and effect analysis; and

update the machine learning algorithms used to generate the score based on the analysis of the execution details and logs.

15. The one or more non-transitory computer-readable media of claim 12, wherein the one or more databases are one of a relational database and a NoSQL database.

16. The one or more non-transitory computer-readable media of claim 15, storing additional instructions that, when executed by the computing device, cause the computing device to:

responsive to determining that the score is below the predetermined threshold, transmit, via the communication interface, to a database administrator computing device, an alert regarding the database request.