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

Award Information
Agency:
Department of Defense
Branch
Office of the Secretary of Defense
Amount:
$743,055.00
Award Year:
2012
Program:
SBIR
Phase:
Phase II
Contract:
N00014-12-C-0425
Award Id:
n/a
Agency Tracking Number:
O2-1203
Solicitation Year:
2010
Solicitation Topic Code:
OSD10-L05
Solicitation Number:
2010.2
Small Business Information
15400 Calhoun Drive, Suite 400, Rockville, MD, -
Hubzone Owned:
N
Minority Owned:
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, Contract&Prop
(301) 294-5221
mjames@i-a-i.com
Research Institution:
Stub




Abstract
The sensor data in the area of Navy Multi-INT could be multi-modal, including acoustic images, ocean profiles, infrared data, biometric data, vehicle tracking information, and facial profiles. Textual information, including intelligence reports or chat logs, will also be considered as sensor data. It is important to develop highly scalable and effective data warehousing and mining methods for in-depth exploitation and analysis of such data so that various information consumers can gain intelligence from different perspectives. To address this importance, IAI is developing a distributed and standards based data cubing, mining system, SensorCube, for large amounts of geospatial sensor data. The SensorCube consists of three layers: data integration, data cubing and mining, and semantic search. In the data integration layer, an XML driven ETL (Extraction, Transformation, and Loading) process is developed to integrate complex sensor data. In the data cubing and mining layer, a powerful distributed computational engine is developed to model multi-dimensional sensor data. In the semantic search layer, the search and query capability is developed to connect the meta data and information summarized in data cubes and mining models. Furthermore, the semantic search and cubing visualization can be accessible across different computational platform such as smart phones and tablets.

* information listed above is at the time of submission.

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