A Distributed and Standard based Data Warehouse and Mining System for Large-Scale Sensor Data

Award Information
Agency:
Department of Defense
Branch
Navy
Amount:
$100,000.00
Award Year:
2011
Program:
SBIR
Phase:
Phase I
Contract:
N00014-11-M-0077
Agency Tracking Number:
O102-L05-4180
Solicitation Year:
2010
Solicitation Topic Code:
OSD10-L05
Solicitation Number:
2010.2
Small Business Information
Intelligent Automation, Inc.
15400 Calhoun Drive, Suite 400, Rockville, MD, -
Hubzone Owned:
N
Socially and Economically Disadvantaged:
N
Woman Owned:
Y
Duns:
161911532
Principal Investigator:
Kaizhi Tang
Senior Research Scientist
(301) 294-5214
ktang@i-a-i.com
Business Contact:
Mark James
Director, Contracts&Proposals
(301) 294-5221
mjames@i-a-i.com
Research Institution:
Stub




Abstract
Intelligent Automation, Inc. (IAI), along with our collaborators propose to develop a Distributed and Standard based Data Warehouse and Mining System for Large-Scale Sensor Data (DSDWM). DSDWM is a synergy of SensorML based database system, streamed and high-dimensional data cubing approaches. Our stream cubing architecture is designed to handle streamed sensor data with quick response and partial computation of data cubes by popular-path cubing. The DSDWM innovations are: (1) SensorML schema database to develop a solid and extendable data storage system for large amount of complex sensor data (2) unique innovative high-dimensional data cubing technology, (3) internal data mining tool that adopts a generic framework of data mining, to provide advanced data exploitation and analysis capability beyond cubing technology, and (4) AJAX on mobile devices to enable asynchronous communication so that the bandwidth is saved. DSDWM offers the key advantage that multi-source sensor input data can be transferred to minimal cubing thus saving computation and storage expense significantly for high-dimensional cubes and prediction cubes provide an automatic and intelligent way to build cubes with optimized accuracy for a large range of prediction models.

* information listed above is at the time of submission.

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