Hardware device based software selection

Patent metadata

Field	Value
Publication number	US-10685294-B2
Application number	US-201715472921-A
Country	US
Kind code	B2
Filing date	Mar 29, 2017
Priority date	Mar 29, 2017
Publication date	Jun 16, 2020
Grant date	Jun 16, 2020

Abstract

A method and system for improving an operation of an automated IT system is provided. The method includes identifying software applications associated with requirements of processes executed by a hardware device with respect to an IT system. An ordered set of software solutions for modifying the software applications is generated. Sampling software code is generated by applying a sampling technique for enabling the hardware device to execute learning software code with respect to database systems. The sampling software code is executed for enabling an automated learning process applying a feature learning technique for identifying a set of software applications and enabling an evaluation of the software applications with respect to organizational parameters for identifying an organizational fitness for the set of software applications. A software application and associated feature are identified and the software application is executed resulting in improved operation of the hardware device.

First claim

What is claimed is:

1. An automated information technology (IT) system operational improvement method comprising:
identifying, by a processor of a hardware device of an IT system, software applications associated with requirements of processes executed by said hardware device with respect to said IT system, wherein said requirements are comprised by metadata;
generating, by said processor based on results of said identifying, an ordered set of software based solutions associated with features to modify said software applications;
generating, by said processor based on software based solutions, sampling software code by applying a syndicated sampling technique, the sampling software code enabling said hardware device to execute cognitive self-learning software code with respect to a plurality of database systems;
executing, by said processor, said sampling software code enabling an automated learning process and applying a feature learning technique to identify a set of applicable software applications of said software applications;
executing, by said processor, said sampling software code enabling an evaluation of said software applications with respect to organizational parameters defined in said metadata and identifying an organizational fitness for said set of applicable software applications;
identifying, by said processor based on results of said executions of said sampling software code, a software application of said set of applicable software applications and an associated feature of said features; and
executing, by said processor, said identified software application resulting in improved operation of said hardware device.

2. The method of claim 1 , further comprising:
extracting, by said processor from said software application, executable code associated with executing rules for enabling said requirements;
executing, by said processor, said executable code resulting in modifying said software application with respect to improving an efficiency and accuracy of said software application;
generating, by said processor in response to said executing, additional executable code; and
combining, by said processor, said additional executable with said software application resulting in said improving said efficiency and accuracy of said software application.

3. The method of claim 1 , further comprising:
generating, by said processor, a memory structure;
executing, by said processor, switching logic with respect to said metadata;
dividing, by said switching logic, said metadata into two differing categories; and
storing, by said processor in said memory structure, said metadata with respect to said two differing categories.

4. The method of claim 3 , wherein said two differing categories comprise a first category associated with new software technology trends and a second category associated with new software industry trends.

5. The method of claim 1 , wherein said requirements are associated with said set of applicable software applications.

6. The method of claim 1 , further comprising:
mapping, by said processor, said requirements to capabilities of said software application.

7. The method of claim 1 , wherein said improved operation of said hardware device comprises an improved processing speed for said processor.

8. The method of claim 1 , wherein said improved operation of said hardware device comprises an improved memory structure of said hardware device, and wherein said improved memory structure enables an improved access speed for accessing data within said improved memory structure via an internal layered structure of said improved memory structure.

9. The method of claim 1 , further comprising: providing at least one support service for at least one of creating, integrating, hosting, maintaining, and deploying computer-readable code in the hardware device, said code being executed by the processor to implement: said identifying said set of applicable software applications, said generating said ordered set, said generating said sampling software code, said executing said sampling software code for enabling an automated learning process, said executing said sampling software code for enabling an evaluation of said software applications, said identifying said software application, and said executing said software application.

10. A computer program product, comprising a computer readable hardware storage device storing a computer readable program code, said computer readable program code comprising an algorithm that when executed by a processor of a hardware device of an information technology (IT) system implements an automated IT system operational improvement method, said method comprising:
identifying, by said processor, software applications associated with requirements of processes executed by said hardware device with respect to said IT system, wherein said requirements are comprised by metadata;
generating, by said processor based on results of said identifying, an ordered set of software based solutions associated with features to modify said software applications;
generating, by said processor based on software based solutions, sampling software code by applying a syndicated sampling technique, the sampling software code enabling said hardware device to execute cognitive self-learning software code with respect to a plurality of database systems;
executing, by said processor, said sampling software code enabling an automated learning process and applying a feature learning technique to identify a set of applicable software applications of said software applications;
executing, by said processor, said sampling software code enabling an evaluation of said software applications with respect to organizational parameters defined in said metadata and identifying an organizational fitness for said set of applicable software applications;
identifying, by said processor based on results of said executions of said sampling software code, a software application of said set of applicable software applications and an associated feature of said features; and
executing, by said processor, said identified software application resulting in improved operation of said hardware device.

11. The computer program product of claim 10 , wherein said method further comprises:
extracting, by said processor from said software application, executable code associated with executing rules for enabling said requirements;
executing, by said processor, said executable code resulting in modifying said software application with respect to improving an efficiency and accuracy of said software application;
generating, by said processor in response to said executing, additional executable code; and
combining, by said processor, said additional executable with said software application resulting in said improving said efficiency and accuracy of said software application.

12. The computer program product of claim 10 , wherein said method further comprises:
generating, by said processor, a memory structure;
executing, by said processor, switching logic with respect to said metadata;
dividing, by said switching logic, said metadata into two differing categories; and
storing, by said processor in said memory structure, said metadata with respect to said two differing categories.

13. The computer program product of claim 12 , wherein said two differing categories comprise a first category associated with new software technology trends and a second category associated with new software industry trends.

14. The computer program product of claim 10 , wherein said requirements are associated with said set of applicable software

Assignees

• IBM

Inventors

• Bharti Harish
• Saxena Rajesh K
• Sreenivasan Balakrishnan

Classifications

• G06N20/00 Primary

  Machine learning · CPC title

• G06F8/355

  Round-trip engineering · CPC title