Methods and systems for hierarchical dynamic cataloging

Patent metadata

Field	Value
Publication number	US-11586596-B2
Application number	US-201916596986-A
Country	US
Kind code	B2
Filing date	Oct 9, 2019
Priority date	Oct 5, 2018
Publication date	Feb 21, 2023
Grant date	Feb 21, 2023

Abstract

Data cataloging has become a necessity for empowering organizations with analytical ability. Conventional cataloging systems may fail to provide proper visualization of data to the different stakeholders of an organization. The present disclosure provides a hierarchical dynamic cataloging system so that visualization of data at different levels would be possible for different stake holders. In the present disclosure, a hierarchical structure of algorithms and multiple stake holders along with relevant metadata is generated. Further, a catalog is generated by performing a mapping across components comprised in the hierarchical structure and identifying relationship across the components based on mapping. The catalog gets dynamically updated and provides a dynamic view of the algorithms and associated metadata to the multiple stakeholders of an organization. Further, the disclosure supports reuse of already developed algorithms across multiple applications and domains resulting in optimization of resources and time.

First claim

What is claimed is:

1. A processor-implemented method for hierarchical dynamic cataloging,
the method comprising:
receiving a plurality of inputs and metadata associated with each of the plurality of inputs from one or more stakeholders pertaining to one or more applications, wherein
the plurality of inputs comprises algorithms, functions, source codes, configuration scripts, and domain knowledge;
tuning one or more hyper-parameters associated with the algorithms pertaining to the one or more applications;
generating a hierarchical structure of the plurality of inputs and the one or more stake holders pertaining to the one or more applications;
performing a mapping across the plurality of inputs received from the one or more stakeholders, based on the hierarchical structure to identify relationship between one or more components specified in one or more applications;
generating a catalog based on the identified relationship, wherein the catalog comprises a hierarchical listing of the algorithms with associated metadata developed for performing one or more tasks pertaining to the one or more applications in a specific domain;
receiving an incoming request from one or more users;
determining, based on the incoming request, a change in one or more attributes defining the hierarchical structure comprised in the catalog, wherein the one or more attributes includes the algorithms and the associated metadata, input parameters, output parameters, execution platforms, type of machine learning models, the functions, the source codes, and the configuration scripts; dynamically updating the catalog based on the change in one or more attributes defining the hierarchical structure and providing a dynamic view of the algorithms and the associated metadata, wherein the one or more components comprised in the hierarchical structure comprise a plurality of algorithms, a plurality of machine learning models, a plurality of execution platforms and information pertaining to one or more stakeholders, wherein the plurality of algorithms are structured in a hierarchy based on a layer of abstractions arranged in a chronological order, wherein the plurality of algorithms include one of a prescriptive algorithm, diagnostic algorithm, predictive algorithm and a prognostic algorithm, the prescriptive algorithm enables prediction for time of occurrence of a failure, the diagnostic algorithm depict step-by-step method for diagnosis using a combination of at least one of symptoms, signs, and test results, the predictive algorithms being used for predictions of future events and unknown events, and the prognostic algorithms assist in improving process of scheduling maintenance, ordering parts and using resources; and
enabling, based on the dynamically updated catalog, reusability of at least one of the plurality of inputs and the one or more components for the incoming request.

2. The processor implemented method of claim 1 , further comprising providing a dynamic view of the algorithms and stakeholders by dynamically updating the catalog in case of any changes in the inputs and the relationship.

3. The processor implemented method of claim 1 , further comprising creating a zero coding dynamic algorithm for an application by enabling reusability of a plurality of modules associated with other applications.

4. A system ( 100 ) for hierarchical dynamic cataloging, the system comprising:
a memory ( 102 );
one or more communication interfaces ( 104 ); and
one or more hardware processors ( 106 ) coupled to said memory through said one or more communication interfaces, wherein said one or more hardware processors are configured to:
receive a plurality of inputs and metadata associated with each of the plurality of inputs from one or more stakeholders pertaining to one or more applications, wherein
the plurality of inputs comprises algorithms, functions, source codes, configuration scripts, and domain knowledge;

tune one or more hyper-parameters associated with the algorithms pertaining to the one or more applications;
generate a hierarchical structure of the plurality of inputs and the one or more stake holders pertaining to the one or more applications;
perform a mapping across the plurality of inputs received from the one or more stakeholders based on the hierarchical structure to identify relationship between one or more components specified in one or more applications;
generate a catalog based on the identified relationship, wherein the catalog comprises a hierarchical listing of the algorithms with associated metadata developed for performing one or more tasks pertaining to the one or more applications in a specific domain;
receive an incoming request from one or more users;
determine, based on the incoming request, a change in one or more attributes defining the hierarchical structure comprised in the catalog, wherein the one or more attributes includes the algorithms and the associated metadata, input parameters, output parameters, execution platforms, type of machine learning models, the functions, the source codes, and the configuration scripts;
dynamically update the catalog based on the change in one or more attributes defining the hierarchical structure and provide a dynamic view of the algorithms and the associated metadata, wherein the one or more components comprised in the hierarchical structure comprise a plurality of algorithms, a plurality of machine learning models, a plurality of execution platforms and information pertaining to one or more stakeholders, wherein the plurality of algorithms are structured in a hierarchy based on a layer of abstractions arranged in a chronological order, wherein the plurality of algorithms include one of a prescriptive algorithm, diagnostic algorithm, predictive algorithm and a prognostic algorithm, the prescriptive algorithm enables prediction for time of occurrence of a failure, the diagnostic algorithm depict step-by-step method for diagnosis using a combination of at least one of symptoms, signs, and test results, the predictive algorithms being used for predictions of future events and unknown events, and the prognostic algorithms assist in improving process of scheduling maintenance, ordering parts and using resources; and
enable, based on the dynamically updated catalog, reusability of at least one of the plurality of inputs and the one or more components for the incoming request.

5. The system of claim 4 , wherein the one or more components comprised in the hierarchical structure comprise a plurality of algorithms, a plurality of machine learning models, a plurality of execution platforms and information pertaining to one or more stakeholders.

6. The system of claim 4 , further configured to provide a dynamic view of the algorithms and stakeholders by dynamically updating the catalog in case of any changes in the inputs and the relationship.

7. The system of claim 4 , further configured to create a zero coding dynamic algorithm for an application by enabling reusability of a plurality of modules associated with other applications.

8. One or more non-transitory machine readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause
receiving a plurality of inputs and metadata associated with each of the plurality of inputs from one or more stakeholders pertaining to one or more applications, wherein
the plurality of inputs comprises algorithms, functions, source codes, configuration scripts, and domain knowledge;

tuning one or more hyper-parameters associated with the algorithms pertaining to the one or more applications;
generating a hierarchical structure of the plurality of inputs and the one or more stake holders pertaining to the one or more

Assignees

• Tata Consultancy Services Ltd

Inventors

• Panda Satanik
• Biswas Dibyendu
• Dutta Hemanta
• Chattopadhyay Tanushyam
• Misra Prateep

Classifications

• G06Q10/105 Primary

  Human resources · CPC title

• G06N20/00

  Machine learning · CPC title

• G06F16/282

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

• G06F16/289

  Object oriented databases · CPC title

• G06F16/2365

  Ensuring data consistency and integrity · CPC title