Dependable Real-Time Data Mining

Authors:
Bhavani Thuraisingham;Latifur Khan;Chris Clifton;John Maurer;Marion Ceruti
Affiliations:
The University of Texas at Dallas/ The MITRE Corporation;The University of Texas at Dallas;The MITRE Corporation;The MITRE Corporation;Space and Naval Warfare Systems Center, San Diego
Venue:
ISORC '05 Proceedings of the Eighth IEEE International Symposium on Object-Oriented Real-Time Distributed Computing
Year:
2005

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Abstract

In this paper we discuss the need for real-time data mining for many applications in government and industry and describe resulting research issues. We also discuss dependability issues including incorporating security, integrity, timeliness and fault tolerance into data mining. Several different data mining outcomes are described with regard to their implementation in a real-time environment. These outcomes include clustering, association-rule mining, link analysis and anomaly detection. The paper describes how they would be used together in various parallel-processing architectures. Stream mining is discussed with respect to the challenges of performing data mining on stream data from sensors. The paper concludes with a summary and discussion of directions in this emerging area.