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Proceedings Paper

Toward ubiquitous mining of distributed data
Author(s): Rajeev Ayyagari; Byong-Hoon Park; Daryl Hershberger; Hillol Kargupta
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Paper 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.

Paper Details

Date Published: 27 March 2001
PDF: 11 pages
Proc. SPIE 4384, Data Mining and Knowledge Discovery: Theory, Tools, and Technology III, (27 March 2001); doi: 10.1117/12.421067
Show Author Affiliations
Rajeev Ayyagari, Univ. of Maryland/Baltimore County (United States)
Byong-Hoon Park, Univ. of Maryland/Baltimore County (United States)
Daryl Hershberger, Univ. of Maryland/Baltimore County (United States)
Hillol Kargupta, Univ. of Maryland/Baltimore County (United States)


Published in SPIE Proceedings Vol. 4384:
Data Mining and Knowledge Discovery: Theory, Tools, and Technology III
Belur V. Dasarathy, Editor(s)

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