KEYWORDS: Databases, Sensors, Java, Visualization, Data integration, Artificial intelligence, Data analysis, Data storage, Web services, Human-machine interfaces
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. Improved
techniques are needed to reduce an analysts decision response time and to enable more intelligent and immediate
situation awareness. This paper describes the Sensor Data and Analysis Framework (SDAF) system built to
provide analysts with the ability to pose integrated queries on diverse live and historical data sources, and plug in
needed algorithms for upstream processing and filtering. The SDAF system was inspired by input and feedback
from field analysts and experts. This paper presents SDAF's capabilities, implementation, and reasoning behind
implementation decisions. Finally, lessons learned from preliminary tests and deployments are captured for
future work.
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