Method and system for modeling data

• US 8,725,754 B2
• Filed: 04/27/2012
• Issued: 05/13/2014
• Est. Priority Date: 05/16/2011
• Status: Active Grant

• Current Assignee: Xurmo Technologies Pvt Ltd.
• Original Assignee: Xurmo Technologies Pvt Ltd.
• Inventors: Gopalakrishnan, Sridhar
• Primary Examiner(s): Girma, Anteneh
• Application Number:
  US13/457,490
• Publication Number:
  US 20130117316A1
• Time in Patent Office: 746 Days
• Field of Search: 707/790, 707/999, 707/764, 707/E17.014, 707/723, 707/726, 707/722, 707/739, 726/6
• US Class Current: 707/764
• CPC Class Codes: G06F 16/288 Entity relationship models

Abstract

The various embodiments herein provide a method and system for modeling a data. The method for modeling data comprises steps of extracting the data from a plurality of data sources, identifying a plurality of entities from the plurality of data, defining occurrence of a relationship between the plurality of entities, capturing recurrences of the relationship between the plurality of entities based on one or more common interactions between the plurality of entities and creating a data model indicating the occurrences and recurrences of the relationship between the plurality of the entities. The data model is adapted to store data corresponding to the plurality of entities, the relationship between the plurality of entities and the common interactions between the plurality of entities. The plurality of entities includes contents of a digital data artifact.

Claims

1. A method for modeling data, the method comprises:

extracting a plurality of data from a plurality of data sources;

identifying a plurality of entities from the plurality of data;

defining occurrence of a relationship between the plurality of the entities;

capturing recurrences of the relationship between the plurality of entities based on one or more common interactions between the plurality of entities, and wherein capturing recurrences of the relationship comprises identifying a data based on a history of one or more interactions between the plurality of entities, detecting one or more entities which are commonly found with respect to captured recurrence of relationship and ascertaining a relevancy of the data by evaluating multiple occurrences of the relationship between the plurality of entities, and wherein the relationship between the plurality of entities is captured with a single notational operation ‘

is related to’

; and

creating a data model indicating the occurrences and recurrences of the relationship between the plurality of the entities;

wherein the data model is adapted to store the data corresponding to the plurality of entities, the relationship between the plurality of entities and the common interactions between the plurality of entities, and wherein the data model extracts and models information from any one the plurality of data sources that are made available to a system, and wherein the data model unites and synthesizes the data from the plurality of data sources to weave a data model in which the plurality of entities and a relationship with the plurality of entities are defined, and wherein the data model is a unified platform for storing all extracted and classified entities in data stores;

wherein an optimization of the data stored in each of the data model is performed using a Unique Resource Identifier of Resource Description Framework (URI-RDF) approach.

2. The method of claim 1 further comprises updating the data model automatically in response to a modification in a context information or an operation history which is associated with at least one of the plurality of entities.

3. The method of claim 1 further comprises qualifying the occurrence of the relationship between the pluralities of entities based on a time frame and a strength, wherein the strength of the plurality of entities is determined based on an identification of a credibility of the plurality of data sources.

4. The method of claim 1 further comprises defining one or more entities associated with the plurality of entities based on an independent existence of the data across the plurality of entities and which is uniquely identified.

5. The method of claim 1, wherein each of the plurality of the entities is connected to other entity by a relationship, and wherein each relationship functions as an entity and wherein the relationship between the plurality of entities is described reciprocally in relation to the other.

6. The method of claim 1, wherein the plurality of data sources comprises at least one of emails, document management systems, database management systems, data warehouses and a plurality of public domain data sources, and wherein the plurality of data sources include structured data sources, semi-structured data sources and unstructured data sources, and wherein structured data sources include databases, and wherein unstructured data sources include emails.

7. The method of claim 1, wherein the plurality of entities includes contents of a digital data artifact, and wherein the digital data artifact includes people, documents, and data which are described in relation to each other, and wherein an object in the digital artifact is an entity, and wherein the object includes paragraphs in a document, sentences in a paragraph, words in a sentence, names of people, rows and columns in a database.

8. The method of claim 1, wherein the plurality of entities is of a type including at least one of a known entity and a derived entity.

9. A computer-implemented system for modeling data, the system comprising:

a data store;

a data extractor module configured to extract a plurality the data from a plurality of data sources;

an entity extractor module configured to define a plurality of entities a plurality of data sources;

a relationship identifier module configured to identify an occurrence of a relationship between the plurality of entities and to evaluate recurrences of the relationship between the plurality of the entities, wherein the relationship identifier module further configured to evolve the relationships between the plurality of entities based on at least one of strength, a context and a frequency of common interactions between a plurality of entities over a time frame, and wherein the relationship identifier module employs at least one of a machine learning algorithm, an inference engine, or a semantic aggregator to evaluate the relationship existing between the plurality of entities and evolve the plurality of entities based on the strength and a time of usage of the contexts; and

a data model generator configured to create a data model from a plurality of words extracted by the data extractor module from the plurality of data sources and the information regarding people and documents extracted by the entity extractor, wherein the data model generator further configured to understand naturally existing relationships between the plurality of entities and a plurality of properties associated with the entities, wherein the data model generator further configured to evolve the plurality of entities based on a strength, a time and a frequency of use of a context;

wherein the data model extracts and models information from any data sources that are made available to a system, and wherein the data model unites and synthesizes data from the plurality of data sources to weave a data model in which the plurality of entities and a relationship with the plurality of entities are defined, and wherein the data model is configured to store a data corresponding to the plurality of entities, occurrence of the relationship between the plurality of entities and recurrences of the relationships between the plurality of entities based on one or more common interactions between the plurality of entities, and wherein the data model is a unified platform for storing all extracted and classified entities in data stores, and wherein the data stored is optimized by interacting by interacting with a plurality of data models using a URI-RDF (Unique Resource Identifier of Resource Description Framework).

10. The system of claim 9, further comprising a data store implemented using one or more data structures, wherein then data structures are adapted to interact with each other for optimization of the data, and wherein the data store comprises a file system, a graph database, a key value store, and an RDBMS which is transparent to the user of the data model.

11. The system of claim 9, wherein the entity extractor is adapted to automatically extract a document data and a metadata from a plurality of structured, semi-structured and unstructured text documents, and wherein the entity extractor is adapted to extract a structured information from a plurality of unstructured machine readable documents and semi-structured machine readable documents, and wherein the metadata includes syntactic and semantic data.

12. The system of claim 9, wherein the relationship identifier includes a one or more machine learning algorithms to evaluate occurrence and recurrences of relationships between the plurality of the entities.

13. The system of claim 9, wherein the data model is adapted to be programmed through an application programming interface to employ a user defined data logic.