Distributed Relational Learning for Cloud Data Fusion

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N00014-14-P-1092
Agency Tracking Number: N132-135-1262
Amount: $79,989.00
Phase: Phase I
Program: SBIR
Awards Year: 2014
Solitcitation Year: 2013
Solitcitation Topic Code: N132-135
Solitcitation Number: 2013.2
Small Business Information
Commonwealth Computer Research, Inc.
1422 Sachem Pl., Unit #1, Charlottesville, VA, 22901
Duns: 809180151
Hubzone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Nicholas Hamblet
 Systems Engineer
 (434) 284-9415
Business Contact
 James Conklin
Title: Director of Operations
Phone: (434) 214-4415
Email: conklin@ccri.com
Research Institution
The US military and intelligence community has been successfully fusing the data it gathers into actionable intelligence. However, the volume of data is increasing such that it cannot be processed on a single server, calling for distributed data fusion algorithms that operate across a cloud. As data grows to the point of requiring distributed storage, machine learning algorithms capable of producing situational awareness must rise to the challenge of working with distributed storage as well. The problem is to design distributed fusion algorithms which not only do as well as single-server solutions, but which leverage larger volumes of data to produce higher quality analytics. This proposal outlines an architecture that works with distributed data sources without needing data to be directly shared between compute nodes. Data fusion without shared memory is a difficult task; however we develop techniques to minimize the amount of information sent between nodes while maintaining high quality fusion. We propose to use models for which both model learning and inference can leverage distributed storage and computation. Inference should be fast and detached model instances readily deployable to local servers for real-time use, while maintaining data and model integrity with the cloud.

* information listed above is at the time of submission.

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