Transformation of data from legacy architecture to updated architecture

Patent metadata

Field	Value
Publication number	US-2023121209-A1
Application number	US-202117491749-A
Country	US
Kind code	A1
Filing date	Oct 1, 2021
Priority date	Oct 1, 2021
Publication date	Apr 20, 2023
Grant date	—

Abstract

One or more systems, computer-implemented methods and/or computer program products to facilitate a process to transform original operational data into updated operational data. A system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a transformation component that can transform original operational data of a first architecture into updated operational data employable at a second architectures, wherein the second architectures is an updated architectures relative to the first architecture. In one or more embodiments, the transformation component further can employ machine learning to match one or more data elements of the original operational data to one or more aspects of the second architecture.

First claim

What is claimed is:

1 . A system, comprising:
a memory that stores computer executable components; and a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise:
a transformation component that transforms original operational data of a first architecture into updated operational data employable at a second architectures, wherein the second architectures is an updated architecture relative to the first architecture.

2 . The system of claim 1 , wherein the transformation component further employs machine learning to cluster one or more data elements of the original operational data relative to one or more aspects of the second architecture.

3 . The system of claim 1 , wherein the transformation component further disentangles a first data element of the original operational data relative to a second data element of the original operational data.

4 . The system of claim 3 , wherein the transformation component further duplicates the first or the second data element for transformation to a pair of a duplicate elements of the updated operational data.

5 . The system of claim 2 , further comprising:
a training component that stores comparison data regarding the clustering in a knowledge database accessible by the transformation component.

6 . The system of claim 1 , further comprising:
a training component that trains a machine learning model based on comparison data comparing the original operational data and the updated operational data, and wherein the machine learning model is employed by the transformation component.

7 . The system of claim 1 , wherein the transformation component further employs one or more data characteristics of the original operational data to establish correlation to one or more data characteristics of the updated architecture.

8 . A computer-implemented method, comprising:
transforming, by a system operatively coupled to a processor, original operational data of a first architecture into updated operational data employable at a second architectures, wherein the second architecture is an updated architectures relative to the first architecture.

9 . The computer-implemented method of claim 8 , further comprising:
employing, by the system, machine learning to cluster one or more data elements of the original operational data to one or more aspects of the second architecture.

10 . The computer-implemented method of claim 8 , further comprising:
disentangling, by the system, a first data element of the original operational data relative to a second data element of the original operational data.

11 . The computer-implemented method of claim 10 , further comprising:
duplicating, by the system, the first or the second data element for transformation to a pair of duplicate elements of the updated operational data.

12 . The computer-implemented method of claim 9 , further comprising:
storing, by the system, comparison data regarding the clustering in a knowledge database accessible for being employed for the transforming.

13 . The computer-implemented method of claim 8 , further comprising:
training, by the system, a machine learning model based on comparison data comparing the original operational data and the updated operational data.

14 . The computer-implemented method of claim 8 ,
wherein the second architecture defines an updated application relative to an application defined by the first architecture.

15 . A computer program product facilitating a process to transform original operational data into updated operational data, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to:
transform, by the processor, the original operational data of a first architecture into the updated operational data employable at a second architectures, wherein the second architecture is an updated architecture relative to the first architecture.

16 . The computer program product of claim 15 , wherein the program instructions executable by the processor further cause the processor to:
employ, by the processor, machine learning to cluster one or more data elements of the original operational data to one or more aspects of the second architecture.

17 . The computer program product of claim 15 , wherein the program instructions executable by the processor further cause the processor to:
disentangle, by the processor, a first data element of the original operational data relative to a second data element of the original operational data.

18 . The computer program product of claim 17 , wherein the program instructions executable by the processor further cause the processor to:
duplicate, by the processor, the first or the second data element for transformation to a pair of a duplicate elements of the updated operational data.

19 . The computer program product of claim 16 , wherein the program instructions executable by the processor further cause the processor to:
store, by the processor, comparison data regarding the clustering in a knowledge database accessible for being employed during the transforming.

20 . The computer program product of claim 15 , wherein the program instructions executable by the processor further cause the processor to:
train, by the processor, a machine learning model based on comparison data comparing the original operational data and the updated operational data.

Assignees

• IBM

Inventors

• Hwang Jinho
• Shwartz Larisa
• Batta Raghav
• Wang Qing
• Aggarwal Pooja
• Gupta Ajay
• Kumar Harshit
• Mohapatra Prateeti

Classifications

• G06N20/00 Primary

  Machine learning · CPC title

• G06N5/022

  Knowledge engineering; Knowledge acquisition · CPC title

• G06F9/541 Primary

  via adapters, e.g. between incompatible applications · CPC title

• G06F9/30181

  Instruction operation extension or modification · CPC title

• G06F18/23213

  with fixed number of clusters, e.g. K-means clustering · CPC title