Identifying patterns of a set of software applications

Patent metadata

Field	Value
Publication number	US-2017193021-A1
Application number	US-201514985557-A
Country	US
Kind code	A1
Filing date	Dec 31, 2015
Priority date	Dec 31, 2015
Publication date	Jul 6, 2017
Grant date	—

Abstract

A mechanism is provided for identifying patterns of a set of software applications instances from their documents. The computer-implemented method begins with constructing different attribute vector types using a knowledge ontology. The knowledge ontology captures semantics based on keywords associated with resource attributes derived from one or more documents related to at least a portion of these software application instances. A knowledge base is built from the attribute vector types and the documents of these application instances. These are merged into the knowledge base with the knowledge base previously built from previous software application instances. Analytics are performed on the knowledge base to identify at least one of common patterns of deployments, configurations, or other attribute vector types, or a combination thereof.

First claim

What is claimed is:

1 . A method for identifying patterns of a set of software applications instances from their documents, the method comprises:
constructing, by a processor, a plurality of different attribute vector types using a knowledge ontology to capture semantics based on keywords associated with resource attributes derived from one or more documents related to at least a portion of these software application instances; building a knowledge base from the plurality of attribute vector types and the documents of these application instances; merging into the knowledge base with the knowledge base previously built from previous software application instances; and performing analytics on the knowledge base to identify at least one of common patterns of deployments, configurations, or other attribute vector types, or a combination thereof.

2 . The method of claim 1 , wherein the constructing, by a processor, a plurality of different attribute vectors types includes using at least one of Analytics for Logical Dependency Mapping (ALDM), Darwin Information Typing Architecture (DITA), or a combination thereof.

3 . The method of claim 1 , wherein the constructing, by a processor, a plurality of different attribute vectors types includes using one or more documents related to at least a portion of a software application that have been previously deployed to a hosted environment.

4 . The method of claim 1 , wherein the performing analytics includes performing analytics with database query inspection technique.

5 . The method of claim 1 , wherein the performing analytics includes performing analytics with machine learning techniques.

6 . The method of claim 1 , wherein the performing analytics includes performing analytics with clustering techniques.

7 . The method of claim 1 , wherein the constructing the plurality of different attribute vectors types further includes using the knowledge ontology to capture a structure of a computing entity involved with the attribute vector.

8 . The method of claim 1 , wherein the constructing the plurality of different attribute vectors types further includes selecting at least one attribute vectors type of:
a deployment vector; a configuration vector; a placement vector; a compute vector; a storage vector; or a combination thereof.

9 . The method of claim 1 , wherein the constructing the plurality of different attribute vectors types further includes selecting at least one attribute vectors type of:
a network routing vector; a security group vector; an availability group vector a load balancing vectors; a backup vectors; a user role and permission vector; or a combination thereof.

10 . The method of claim 1 , wherein the at least one of common sets of patterns are used to identify what deployments, configurations, and/or other attribute vectors the application instances will be in after they are to migrated from an enterprise environment to a hosted environment.

11 . The method of claim 10 , wherein the at least one of common sets of patterns are applied to one of cloud service catalog, cloud machine images, cloud workflows, and cloud design implementations.

12 . The method of claim 1 , wherein the at least one of common sets of patterns are used to identify small number of application instances the application instances can be consolidated into.

13 . The method of claim 1 , wherein the at least one of common patterns of deployments, configurations, or other attribute vector types configurations include both domain knowledge and middleware-specific knowledge.

14 . The method of claim 1 , wherein the at least one of common patterns of deployments, configurations, or other attribute vector types configurations include multi-layer knowledge in which configurations and attributes are specific to middleware and an abstract layer for individual types of middleware.

15 . The method of claim 1 , wherein the building the knowledge base from the plurality of attribute vector types and the documents of these application instances includes using unified modeling language (UML).

16 . The method of claim 1 , wherein the constructing, by the processor, the plurality of different attribute vector types using the knowledge ontology includes using the knowledge ontology that is one of generic, domain specific, or middleware specific.

17 . An apparatus comprising:
a processor; and a memory coupled to the processor, wherein the memory comprises instructions which, when executed by the processor, cause the processor to perform: constructing, by a processor, a plurality of different attribute vector types using a knowledge ontology to capture semantics based on keywords associated with resource attributes derived from one or more documents related to at least a portion of these software application instances; building a knowledge base from the plurality of attribute vector types and the documents of these application instances; merging into the knowledge base with the knowledge base previously built from previous software application instances; and performing analytics on the knowledge base to identify at least one of common patterns of deployments, configurations, or other attribute vector types, or a combination thereof.

18 . The apparatus of claim 17 , wherein the constructing, by a processor, a plurality of different attribute vectors types includes using at least one of Analytics for Logical Dependency Mapping (ALDM), Darwin Information Typing Architecture (DITA), or a combination thereof.

19 . The apparatus of claim 17 , wherein the constructing, by a processor, a plurality of different attribute vectors types includes using one or more documents related to at least a portion of a software application that have been previously deployed to a hosted environment.

20 . A computer program product for identifying patterns of a set of software applications instances from their documents comprising a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a computing device, causes the computing device to perform:
accessing, by a processor, a description of a domain model that incorporates both behavior and data; constructing, by a processor, a plurality of different attribute vector types using a knowledge ontology to capture semantics based on keywords associated with resource attributes derived from one or more documents related to at least a portion of these software application instances; building a knowledge base from the plurality of attribute vector types and the documents of these application instances; merging into the knowledge base with the knowledge base previously built from previous software application instances; and performing analytics on the knowledge base to identify at least one of common patterns of deployments, configurations, or other attribute vector types, or a combination thereof.

Assignees

• IBM

Inventors

• Deng Yu
• Ionescu Theodor Razvan
• Kofkin-Hansen Simon J
• Mahindru Ruchi
• Moss Christopher
• Ramasamy Harigovind Venkatraj
• Sarkar Soumitra
• SMITH Richard Christopher
• Wang Long

Classifications

• G06F8/63 Primary

  Image based installation; Cloning; Build to order · CPC title

• G06Q10/06

  Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling · CPC title

• G06F16/2228 Primary

  Indexing structures · CPC title

• G06F16/93

  Document management systems · CPC title

• G06F17/30321 Primary

  Physics · mapped topic