System and Method for a Big Data Analytics Enterprise Framework

• US 20170053242A1
• Filed: 08/18/2015
• Published: 02/23/2017
• Est. Priority Date: 08/18/2015
• Status: Active Grant

• Current Assignee: MA FOI Analytics and Business Services Private Limited
• Original Assignee: MA FOI Analytics and Business Services Private Limited
• Inventors: AYYASWAMI, SATISH
• Granted Patent: US 10,643,181 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/22 Indexing; Data structures therefor; Storage structures
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 8/20 Software design
• CPC Class Codes: G06F 8/34 Graphical or visual programming
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06Q 10/103 Workflow collaboration or project management
• CPC Class Codes: H04L 63/105 Multiple levels of security

Abstract

A method and system for providing big data analytics framework for predictive and qualitative analysis for application developers, data scientists and system engineers without having technology specific programming experience. Further the framework contains adapters for the software engineers to configure the big data hub, wherein these software engineers can easily share, store, process and predict functionalities with the data scientists and user interface (UI) developers.

Claims

1. A system for providing big data analytics framework for predictive and qualitative analysis, said system comprising:

a) a standardized web-based explorer interface module configured to;

receive database schema from a business user and a software development kit (SDKs) provided to software developers to utilize said explorer interface to develop, test, store, manage and deploy the said database schema;

b) a pre-defined or custom adapter module based adapter module configured to;

ingest data to the said database by the software engineer as assigned by the business user;

c) a processor manager module configured to;

process the received data based on request from the business user using defined metrics; and

further the software engineer completes the task and deploys the processor;

d) a predict manager module configured to;

assign an analytic task to a data scientist, wherein the data scientist uses a curl tool to pull the data via REST (Representational State Transfer) architectural style APIs, and once the data is built the data scientist deploys a scoring engine using the curl tool;

e) a user interface module configured to;

extract data by a user interface designer assigned by the business user for visualization associated with lead scoring, sales forecasting and other relevant store, process and predict functionalities of big data.

2. The system of claim 1, wherein the database schema is stored in form of structured and unstructured big data.

3. The system of claim 1, wherein the modules are dynamically coupled with framework and are seamlessly integrated for ad-hoc big data gathering.

4. The system of claim 1, wherein the modules are being scalable at an instance by means of dynamic addition of servers capable of processing data depending on the load on the present servers handling big data.

5. The system of claim 1, wherein each of the modules are created, tested initiated, stopped, restarted upgraded, modified, deleted, deployed, and un-deployed based on the request from the application developers or software engineers or data scientists or business user.

6. The system of claim 1, wherein the framework is equipped with monitoring, management and control of service setup, software and hardware setup implemented by means of standard web-based portals in the framework.

7. The system of claim 1, wherein the framework is configured to enable identity management, policy driven access control, data privacy controls and authentication of various levels accessing the framework.

8. A method for providing big data analytics framework characterized in providing predictive and qualitative analysis, the method comprising steps of:

a) providing a standardized web-based explorer interface module to receive database schema from a business user and a software development kit (SDKs) provided to software developers to utilize said explorer interface to develop, test, store, manage and deploy the said database schema;

b) configuring a pre-defined or custom based adapter module to ingest data to the said database by the software engineer as assigned by the business user;

c) providing a processor manager configures to process the received data based on request from the business user using defined metrics; and

further the software engineer completes the task and deploys the processor;

d) providing a predict manager module configured to assign an analytic task to a data scientist, wherein the data scientist uses a curl tool to pull the data via REST (Representational State Transfer) architectural style APIs, and once the data is built the data scientist deploys a scoring engine using the curl tool;

e) providing a user interface module configured to extract data by a user interface designer assigned by the business user for visualization associated with lead scoring, sales forecasting and other relevant store, process and predict functionalities of big data.

9. The method of claim 8, wherein the database schema is stored in form of structured and unstructured big data.

10. The method of claim 8, wherein the modules are dynamically coupled with framework and are seamlessly integrated for ad-hoc big data gathering.

11. The method of claim 8, wherein the modules are being scalable at an instance by means of dynamic addition of servers capable of processing data depending on the load on the present servers handling big data.

12. The method of claim 8, wherein each of the modules are created, tested initiated, stopped, restarted upgraded, modified, deleted, deployed, and un-deployed based on the request from the application developers or software engineers or data scientists or business user.

13. The method of claim 8, wherein the framework is equipped with monitoring, management and control of service setup, software and hardware setup implemented by means of standard web-based portals in the framework.

14. The method of claim 8, wherein the framework is configured to enable identity management, policy driven access control, data privacy controls and authentication of various levels accessing the framework.