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Published Articles >> Table of Contents >> Abstract
Seventh IEEE International Symposium on Multimedia (ISM'05)
p. 4
Mining Evolving Streams with Resource Adaptive Computation
Philip S. Yu, IBM T. J. Watson
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ISM.2005.79
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| Abstract |
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The problem of streaming data has gained importance in recent years because of
advances in hardware technology. The ubiquitous presence of data streams in a number
of practical domains has generated a lot of research in this area. Example applications
include surveillance for terrorist attack, network monitoring for intrusion detection, and
others. Problems such as data mining which have been widely studied for traditional data
sets cannot be easily solved for the data stream domain. This is because the large volume
of data arriving in a stream renders most algorithms to inefficient as most mining
algorithms require multiple scans of data which is unrealistic for stream data. More
importantly, the characteristics of the data stream can change over time and the evolving
pattern needs to be captured. Furthermore, we also need to consider the problem of
resource allocation in mining data streams. Due to the large volume and the high speed of
streaming data, mining algorithms must cope with the effects of system overload. Thus,
how to achieve optimum results under various resource constraints becomes a
challenging task. In this talk, I’ll provide an overview, discuss the issues and focus on
how to mine evolving data streams and perform resource adaptive computation.
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 Additional Information
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Citation:
Philip S. Yu,
"Mining Evolving Streams with Resource Adaptive Computation,"
ism,
p. 4,
Seventh IEEE International Symposium on Multimedia (ISM'05),
2005
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