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Eighth IEEE International Symposium on Object-Oriented Real-Time Distributed Computing (ISORC'05)
Dependable Real-Time Data Mining
Seattle, Washington
May 18-May 20
ISBN: 0-7695-2356-0
Bhavani Thuraisingham, The University of Texas at Dallas; The MITRE Corporation
Latifur Khan, The University of Texas at Dallas
Chris Clifton, The MITRE Corporation
John Maurer, The MITRE Corporation
Marion Ceruti, Space and Naval Warfare Systems Center, San Diego
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.
Citation:
Bhavani Thuraisingham, Latifur Khan, Chris Clifton, John Maurer, Marion Ceruti, "Dependable Real-Time Data Mining," isorc, pp.158-165, Eighth IEEE International Symposium on Object-Oriented Real-Time Distributed Computing (ISORC'05), 2005
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