Improving data analysis through diverse data source integration

Authors:
Ronald Albuquerque;Jennifer Casper;Ed Cheung;Ron Couture;Barry Lai;Peter Leveille;Jing Hu;Dan Mauer
Affiliations:
The MITRE Corporation, Bedford, MA;The MITRE Corporation, Bedford, MA;The MITRE Corporation, Bedford, MA;The MITRE Corporation, Bedford, MA;The MITRE Corporation, Bedford, MA;The MITRE Corporation, Bedford, MA;The MITRE Corporation, Bedford, MA;The MITRE Corporation, Bedford, MA
Venue:
MILCOM'09 Proceedings of the 28th IEEE conference on Military communications
Year:
2009

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Abstract

Daily sensor data volumes are increasing from gigabytes to multiple terabytes. The manpower and resources needed to analyze the increasing amount of data are not growing at the same rate. Current volumes of diverse data, both live streaming and historical, are not fully analyzed. Analysts are left mostly to analyzing the individual data sources manually. This is both time consuming and mentally exhausting. Expanding data collections only exacerbate this problem. Improved data management techniques and analysis methods are required to process the increasing volumes of historical and live streaming data sources simultaneously. Analysts need improved techniques to reduce an decision response time and to enable more intelligent and immediate situation awareness. Faster analysis of disparate information sources may be achieved by providing a system that allows analysts to pose integrated queries on diverse data sources without losing data provenance.