System and method of reduction of irrelevant information during search

Patent metadata

Field	Value
Publication number	US-9760642-B2
Application number	US-201514797069-A
Country	US
Kind code	B2
Filing date	Jul 10, 2015
Priority date	Nov 26, 2012
Publication date	Sep 12, 2017
Grant date	Sep 12, 2017

Abstract

A system including a context-entity factory configured to build a data model defining an ontology of data objects that are context-aware, the model further defining metadata tags for the data objects. The system further includes a storage device storing the data objects as stored data objects, the device further storing associated contexts for corresponding ones of the stored objects. The system further includes a reduction component configured to capture a current context value of a first data object defined in the ontology, the component further configured to compare the current context value of the first data object with stored values of the associated contexts, and wherein when the current context value does not match a particular stored value of a particular associated context, the component is further configured to remove a corresponding particular stored data object and the particular associated context from the stored data objects.

First claim

What is claimed is:

1. A computer system comprising a reduction server in a processor unit comprising:
a context-entity factory configured to:
build a data model defining an ontology of data objects that are context-aware;
text mine activity patterns on a first body of data comprising open-source unstructured text; and
constraint-based mine activity patterns on a second body of data drawn from multiple heterogeneous sources, such that the data model further defines metadata tags for the data objects, and the reduction server communicates with a storage device configured to store the data objects as stored data objects, the storage device further configured to store associated contexts for corresponding ones of the stored data objects; and

a reduction component configured to:
employ at least one information mashup application configured to access a spatial-temporal query engine to generate an output of contextual relationships of multiple spatial data and temporal data objects;
capture a current context value of a first data object defined in the ontology of data objects; and
compare the current context value of the first data object with stored values of the associated contexts, such that when the current context value does not match a particular stored value of a particular associated context in the associated contexts, the reduction component is further configured to remove a corresponding particular stored data object and the particular associated context from the stored data objects.

2. The computer system of claim 1 , wherein the data objects are associated with persons and contextual information comprises at least one of location, identity, time and activity.

3. The computer system of claim 1 , wherein the current context value of the first data object is captured from at least one unstructured text-based data service, and the storage device comprises at least one of: an optical disk, a magnetic disk, a hard drive, and a thumb drive.

4. The computer system of claim 1 , wherein the context-entity factory is further configured to perform a tensor analysis of a plurality of unstructured data objects to derive contextual relationships among the unstructured data objects as a first step of building the data model.

5. The computer system of claim 4 , wherein the context-entity factory is further configured to perform constraint-based mining of activity patterns of data associated with the unstructured data objects to determine the activity patterns representing the contextual relationships among the unstructured data objects as a second step of building the data model.

6. The computer system of claim 5 , wherein:
the data is sourced from multiple sources;
the computer system further comprises one of a publisher and an integrator;
the publisher is configured to one of publish an output of the reduction component as a service in a service oriented architecture and publish the output to at least one of an enterprise portal, an application, a development tool; and
the integrator is configured to interoperate an output of the reduction component with an enterprise application technology selected from at least one of a security application, a governance application, a monitoring application, and an availability application.

7. The computer system of claim 1 , wherein the reduction component is further configured to employ at least one information mashup application to yield a reduced number of data objects relative to a beginning number of data objects, and wherein the computer system further comprises:
a query component configured to query the reduced number of data objects.

8. The computer system of claim 1 , wherein the at least one information mashup application is configured to combine the output of contextual relationships from the spatial-temporal query engine with data drawn from at least one heterogeneous information source to yield a reduced number of data objects relative to a beginning number of data objects.

9. The computer system of claim 1 , wherein the reduction component is configured to filter irrelevant information using an associative memory based on the output of contextual relationships generated by the at least one information mashup application.

10. A computer system comprising a reduction server in a processor unit comprising:
a context-entity factory configured to:
build a data model defining an ontology of data objects that are context-aware;
build, using at least one contextual query engine, at least one contextual query filter;
text mine activity patterns on a first body of data comprising open-source unstructured text, and submit an output of text mining of activity patterns and output of constraint-based mining of activity patterns to at least one contextual query engine; and
constraint-based mine activity patterns on a second body of data drawn from multiple heterogeneous sources, such that the data model further defines metadata tags for the data objects, and the reduction server communicates with a storage device configured to store the data objects as stored data objects, the storage device further configured to store associated contexts for corresponding ones of the stored data objects; and

a reduction component configured to:
employ at least one information mashup application configured to access a spatial-temporal query engine to generate an output of contextual relationships of multiple spatial data and temporal data objects;
capture a current context value of a first data object defined in the ontology of data objects; and
compare the current context value of the first data object with stored values of the associated contexts, such that when the current context value does not match a particular stored value of a particular associated context in the associated contexts, the reduction component is further configured to remove a corresponding particular stored data object and the particular associated context from the stored data objects.

11. A computer system comprising a reduction server in a processor unit comprising:
a context-entity factory configured to:
build a data model defining an ontology of data objects that are context-aware;
build, using at least one contextual query engine, at least one contextual query filter, and provide the contextual query filter to at least one information mashup application for combination with at least one semantic query template, such that at least one application mashup is configured to combine the at least one contextual query filter and at least one semantic query template to produce at least one refined semantic query;
text mine activity patterns on a first body of data comprising open-source unstructured text, and submit an output of text mining of activity patterns and output of constraint-based mining of activity patterns to at least one contextual query engine; and
constraint-based mine activity patterns on a second body of data drawn from multiple heterogeneous sources, such that the data model further defines metadata tags for the data objects, and the reduction server communicates with a storage device configured to store the data objects as stored data objects, the storage device further configured to store associated contexts for corresponding ones of the stored data objects; and

a reduction component configured to:
employ at least one information mashup application configured to access a spatial-temporal query engine to generate an output of contextual relationships of multiple spatial data and temporal data objects;
capture a current context value of a first data object defined in the ontology of data objects; and
compare the current context value of the first data object with s

Assignees

• Boeing Co

Inventors

• Bahrami Ali
• Yuan Jun

Classifications

• G06F40/205

  Parsing · CPC title

• G06F16/9537

  Spatial or temporal dependent retrieval, e.g. spatiotemporal queries · CPC title

• G06F16/9535 Primary

  Search customisation based on user profiles and personalisation · CPC title

• G06F16/289

  Object oriented databases · CPC title

• G06F7/36

  Combined merging and sorting · CPC title