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27 March 2001 Toward ubiquitous mining of distributed data
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Abstract
The role of data-centric information is becoming increasingly important in our everyday professional and personal lives. The advent of laptops, palmtops, handhelds, and wearable computers is also making ubiquitous access to large quantity of data possible. Advanced analysis of distributed data for extracting useful knowledge is the next natural step in the world of ubiquitous computing. However, this will not come for free; it will introduce additional cost due to communication, computational, security among others. Distributed data mining techniques offer a technology to analyze distributed data by minimizing this cost to maintain the ubiquitous presence. This paper adopts the Collective Data Mining approach that offers a collection of different scalable and distributed data analysis techniques. It particularly focuses on two collective techniques for predictive data mining, presents some experimental results, and points the readers toward more extensive documentations of the technology.
© (2001) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Rajeev Ayyagari, Byong-Hoon Park, Daryl Hershberger, and Hillol Kargupta "Toward ubiquitous mining of distributed data", Proc. SPIE 4384, Data Mining and Knowledge Discovery: Theory, Tools, and Technology III, (27 March 2001); https://doi.org/10.1117/12.421067
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