Designing Large Data Handling Architectures

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N00014-10-M-0085
Agency Tracking Number: O092-SP4-4132
Amount: $99,993.00
Phase: Phase I
Program: SBIR
Awards Year: 2010
Solicitation Year: 2009
Solicitation Topic Code: OSD09-SP4
Solicitation Number: 2009.2
Small Business Information
562 E. Weddell Drive, Suite 4, Sunnyvale, CA, 94089
DUNS: 064744436
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Richard Clements
 Senior Software Engineer
 (408) 734-9392
Business Contact
 Bernard Laskowski
Title: President
Phone: (408) 734-9392
Research Institution
Global War on Terror requires critical need for accessing intelligence information databases for actionable decision processes. To enhance intelligence information based decision making a methodology must be established for storage and retrieval from multiple database systems, and subsequent analysis based on information’s meaning rather than predetermined manually assigned categories. Open and standards based architectures are needed to efficiently assemble large amounts of data with greater agile information sharing strategies. Automation of handling large data amounts can be achieved by using metadata, alignment of vocabularies, data sharing governance rules, and defined business processes. Analatom proposes investigating a more robust query and index paradigm having large data handling architectures focusing on ‘Concept Footprints’. Associated pools of data leave ‘multiple’ tracks of variously weighted associations through historical usage and prior user interest. Proposed software will allow multidimensional associations to form (organize and attract to similar concepts and associations) within like concept neighborhoods. These resulting (task oriented) multiple architectures are referred to as ‘Information/Knowledge Cubes’. These then afford access into extremely large data sets and repositories to be concept oriented. Additionally, queries need not be specific or limiting, but rather can be presented to data repository (Knowledge Cube) as incomplete or ‘fuzzy’ textural queries.

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