Self-management of data applications

• US 10,296,502 B1
• Filed: 08/24/2015
• Issued: 05/21/2019
• Est. Priority Date: 08/24/2015
• Status: Active Grant

• Current Assignee: State Farm Mutual Automobile Insurance Company (State Farm)
• Original Assignee: State Farm Mutual Automobile Insurance Company (State Farm)
• Inventors: DeBo, Gregory James, Pillai, Vaidya Balasubramanian, Reiff, Randy, Sheley, Mary Jane
• Primary Examiner(s): Richardson, James E
• Application Number:
  US14/834,033
• Time in Patent Office: 1,366 Days
• 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 16/2365 Ensuring data consistency and integrity
• CPC Class Codes: G06F 16/958 Organisation or management of web site content, e.g. publishing, maintaining pages or automatic linking

Abstract

The present embodiments relate generally to the enforcement of guidelines, standards, and best practices for software applications. According to certain aspects, a method of retrieving, by a self-management engine from a data storage device, health data indicative of a utilization of a collection of data by one or more computing devices is disclosed, including detecting, by the self-management engine, a pattern in the utilization of the collection of data based on the health data; automatically determining, by the self-management engine, a correction to be applied to the collection of data based on the detected pattern and based on one or more rules corresponding to the detected pattern; and causing, by the self-management engine, the correction to be applied to the collection of data.

Claims

1. A method comprising:

retrieving, by a self-management engine from a data storage device, health data indicative of a utilization of a collection of data by one or more computing devices;

identifying, by analyzing the health data, one or more errors occurring while an application utilizes the collection of data over a first period of time;

learning, by analyzing one or more log files generated while the application utilizes the collection of data over the first period of time, a new pattern in the utilization of the collection of data, the pattern associated with the occurrence of errors;

detecting, by the self-management engine, the learned pattern in a utilization of the collection of data over a second period of time;

automatically determining, by the self-management engine, a correction to be applied to the collection of data based on the detected pattern and based on one or more rules corresponding to the detected pattern;

causing, by the self-management engine, the correction to be applied to the collection of data;

transmitting, from the self-management engine to a remote computing device, an indication of the correction to be applied to the collection of data; and

in response to the transmission of the indication of the correction, receiving, at the self-management engine, an approval of the correction;

and in response to receiving the approval, causing, by the self-management engine, a deviation detection script to be stored, wherein the deviation detection script is to be executed at future times to detect the pattern;

wherein causing the correction to be applied to the collection of data is in response to receiving the approval of the correction.

2. The method of claim 1, wherein detecting the pattern includes:

analyzing errors of the collection of data with the learning module to identify the pattern with a corresponding confidence value,

analyzing, with the learning module, other portions of the health data to iteratively update the corresponding confidence value, and

determining, based on the updated corresponding confidence value, that the pattern is detected.

3. The method of claim 1, wherein automatically determining the correction to be applied to the collection of data includes:

matching the detected pattern to the one or more rules, stored on a data storage device, wherein each of the one or more rules indicates an action to take to correct the collection of data when the pattern is detected, and

generating, based on the one or more rules, the correction to be applied to the the collection of data, wherein the correction includes at least some of the actions indicated by the one or more rules.

4. The method of claim 1, wherein the health data includes one or more performance metrics measuring the utilization of the collection of data.

5. The method of claim 1, wherein the collection of data includes a structure and various data entries within the structure, and wherein causing the correction to be applied to the collection of data including altering at least one of the structure or the various data entries within the structure.

6. The method of claim 1, wherein causing the correction to be applied to the at least one of the collection of data includes causing the correction to be applied to both the collection of data and the one or more applications.

7. The method of claim 6, wherein the one or more applications include one or more queries of the collection of data, and wherein causing the correction to be applied to the one or more applications including modifying the queries of the collection of data.

8. The method of claim 1, wherein the health data includes logs of errors generated in the utilization of the collection of data.

9. The method of claim 1, wherein the pattern includes a repetition of an event, wherein the event occurs during the utilization of the collection of data.

10. The method of claim 1, wherein the pattern includes a change in or characteristic of a performance of the one or more applications during the utilization of the collection of data.

13. The system of claim 7, wherein the health data includes one or more performance metrics measuring the utilization of the collection of data.

11. A system comprising:

a non-transitory computer-readable medium storing instructions thereon; and

one or more processors specially configured by the instructions stored on the non-transitory computer-readable medium to;

receive health data indicative of a utilization of a collection of data by one or more computing devices executing one or more applications,

identify, by analyzing the health data, one or more errors occurring while one of the one or more applications utilizes the collection of data over a first period of time;

learn, by analyzing one or more log files generated while the application utilizes the collection of data over the first period of time, a new pattern in the utilization of the collection of data, the pattern associated with the occurrence of errors;

detect the learned pattern in the utilization of the collection of data over a second period of time,

automatically determine a correction to be applied to at least one of the collection of data or the one or more applications based on the detected pattern and based on one or more rules corresponding to the detected pattern, and

cause the correction to be applied to the collection of data;

transmit, to a remote computing device, an indication of the correction to be applied to the collection of data; and

receive, in response to the transmission of the indication of the correction, an approval of the correction;

cause, in response to receiving the approval, a deviation detection script to be stored, wherein the deviation detection script is to be executed at future times to detect the pattern;

wherein causing the correction to be applied to the collection of data is in response to receiving the approval of the correction.

12. The system of claim 11, wherein the health data includes logs of errors generated in the utilization of the collection of data.

14. The system of claim 11, wherein the collection of data includes a structure and various data entries within the structure, and wherein causing the correction to be applied to the collection of data including altering at least one of the structure or the various data entries within the structure.

15. The system of claim 11, wherein causing the correction to be applied to the collection of data includes causing the correction to be applied to both the collection of data and the one or more applications.

16. The system of claim 15, wherein the one or more applications include one or more queries of the collection of data, and wherein causing the correction to be applied to the one or more applications including modifying the queries of the collection of data.

17. The system of claim 11, wherein the pattern includes at a repetition of an event, wherein the event occurs during the utilization of the collection of data.

18. The system of claim 11, wherein the pattern includes a change in or characteristic of a performance of the one or more applications during the utilization of the collection of data.

19. The system of claim 11, wherein detecting the pattern includes:

analyzing errors of the collection of data with the learning module to identify the pattern with a corresponding confidence value,

analyzing, with the learning module, other portions of the health data to iteratively update the corresponding confidence value, and

determining, based on the updated corresponding confidence value, that the pattern is detected.

20. The system of claim 11, wherein automatically determining the correction to be applied to the collection of data includes:

matching the detected pattern to the one or more rules, stored on a data storage device, wherein each of the one or more rules indicates an action to take to correct the collection of data when the pattern is detected, and

generating, based on the one or more rules, the correction to be applied to the collection of data, wherein the correction includes at least some of the actions indicated by the one or more rules.