Cognitive analysis of hierarchical database elements for generation of microservices

Patent metadata

Field	Value
Publication number	US-11880387-B2
Application number	US-202117303720-A
Country	US
Kind code	B2
Filing date	Jun 6, 2021
Priority date	Jun 6, 2021
Publication date	Jan 23, 2024
Grant date	Jan 23, 2024

Abstract

A computer identifies, within a hierarchical database, data elements associated with a selected function associated with the database, comprising. The computer identifies at least one function associated with a hierarchical database containing data elements. The computer, in response to identifying the function, identifies within a list of indica, at least one reference indicia corresponding to the at least one function. The computer identifies within a monolithic application relevant code elements associated with the reference indicia. The computer generates an activity log associated with execution of the relevant code elements. The computer identifies, within the activity log, a group of data elements associated with the execution of the relevant code elements. The computer generates a group data element clusters using a Machine Learning algorithm. The computer identifies at least one of the group of data element clusters as relevant to the at least one function.

First claim

What is claimed is:

1. A computer implemented method to identify within a hierarchical database, data elements associated with a selected function associated with the database, comprising:
identifying by a computer, at least one function of a plurality of functions associated with a hierarchical database containing data elements;
responsive to identifying said function, identifying by said computer within a list of indica available to said computer, at least one reference indicia corresponding to said at least one function;
identifying, by said computer, within a monolithic application including code elements adapted to interact with said hierarchical database, relevant code elements associated with said reference indicia;
generating, by said computer, an activity log associated with execution of said relevant code elements;
identifying, by said computer, within said activity log, a plurality of data elements associated with said execution of said relevant code elements;
identifying, by said computer, transaction clusters as candidates for microservice applications, and one or more of the transaction clusters includes transactions related to the plurality of functions;
generating a model, by said computer, using a Machine Learning algorithm, the model including a plurality of data element clusters, the data element clusters including similar data element groups, and the model identifying entities associated with relevant functions of the plurality of functions;
identifying using the model, by said computer, at least one of said plurality of data element clusters as relevant to said at least one of the relevant functions of the plurality of functions; and
assigning each transaction in at least one of said relevant data element clusters to the at least one relevant function, at least in part, based on similarity of data element groups associated with the execution of the at least one relevant function during an operation for each of the transactions.

2. The method of claim 1 wherein said relevant code elements are selected, at least in part, on meeting a predetermined sufficiency threshold.

3. The method of claim 1 wherein the list of indicia is based, at least in part, on code elements included in said monolithic application.

4. The method of claim 1 wherein the list of indica is based, at least in part, on content within the activity log.

5. The method of claim 1 wherein said ML method is a K-Nearest Neighbor algorithm.

6. The method of claim 1 wherein said clusters are based, at least in part, on a measured Hamming distance among said plurality of data elements.

7. The method of claim 1 wherein a training dataset for said ML algorithm includes data pairs associating said relevant data elements with said least one reference indicia.

8. A system to identify within a hierarchical database, data elements associated with a selected function associated with the database, which comprises:
a computer system comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to:
identify at least one function of a plurality of functions associated with a hierarchical database containing data elements;
responsive to identifying said function, identifying within a list of indica available to said computer, at least one reference indicia corresponding to said at least one function;
identify within a monolithic application including code elements adapted to interact with said hierarchical database, relevant code elements associated with said reference indicia;
generate an activity log associated with execution of said relevant code elements;
identify within said activity log, a plurality of data elements associated with said execution of said relevant code elements;
identifying, by said computer, transaction clusters as candidates for microservice applications, and one or more of the transaction clusters includes transactions related to the plurality of functions;
generate a model using a Machine Learning algorithm, the model including a plurality of data element clusters, the data element clusters including similar data element groups, and the model identifying entities associated with relevant functions of the plurality of functions;
identify using the model, at least one of said plurality of data element clusters as relevant to said at least one of the relevant functions of the plurality of functions; and
assign each transaction in at least one of said relevant data element clusters to the at least one relevant function, at least in part, based on similarity of data element groups associated with the execution of the at least one relevant function during an operation for each of the transactions.

9. The system of claim 8 wherein said relevant code elements are selected, at least in part, on meeting a predetermined sufficiency threshold.

10. The system of claim 8 wherein the list of indicia is based, at least in part, on code elements included in said monolithic application.

11. The system of claim 8 wherein the list of indica is based, at least in part, on content within the activity log.

12. The system of claim 8 wherein said ML method is a K-Nearest Neighbor algorithm.

13. The system of claim 8 wherein said clusters are based, at least in part, on a measured Hamming distance among said plurality of data elements.

14. The system of claim 8 wherein a training dataset for said ML algorithm includes data pairs associating said relevant data elements with said least one reference indicia.

15. A computer program product to identify within a hierarchical database, data elements associated with a selected function associated with the database, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to:
identify within a hierarchical database, data elements associated with a selected function associated with the database, which comprises:
a computer system comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to:
identify, using the computer, at least one function of a plurality of functions associated with a hierarchical database containing data elements;
responsive to identifying said function, identifying using the computer, within a list of indica available to said computer, at least one reference indicia corresponding to said at least one function;
identify, using the computer, within a monolithic application including code elements adapted to interact with said hierarchical database, relevant code elements associated with said reference indicia;
generate, using the computer, an activity log associated with execution of said relevant code elements;
identify, using the computer, within said activity log, a plurality of data elements associated with said execution of said relevant code elements;
identifying, by said computer, transaction clusters as candidates for microservice applications, and one or more of the transaction clusters includes transactions related to the plurality of functions;
generate a model, using the computer, using a Machine Learning algorithm, the model including a plurality of data element clusters, the data element clusters including similar data element groups, and the model identifying entities associated with relevant functions of the plurality of functions;
identify using the model, using the computer, at least one of sa

Assignees

• IBM

Inventors

• Bharti Harish
• Sreenivasan Balakrishnan
• Banerjee Joydeep
• Sukhija Sandeep
• Jain Shweta

Classifications

• G06F16/282 Primary

  Hierarchical databases, e.g. IMS, LDAP data stores or Lotus Notes · CPC title

• G06F16/217

  Database tuning (G06F16/2282 takes precedence; database performance monitoring G06F11/3409) · CPC title

• G06F16/2358

  Change logging, detection, and notification (replication G06F16/27) · CPC title

• G06F18/22 Primary

  Matching criteria, e.g. proximity measures · CPC title

• G06F18/24147

  Distances to closest patterns, e.g. nearest neighbour classification · CPC title