The Community for Technology Leaders
SC Conference (2002)
Baltimore, Maryland
Nov. 16, 2002 to Nov. 22, 2002
ISSN: 1063-9535
ISBN: 0-7695-1524-X
pp: 67
Marco Mazzucco , University of Illinois at Chicago
Asvin Ananthanarayan , University of Illinois at Chicago
Robert L. Grossman , University of Illinois at Chicago
Jorge Levera , University of Illinois at Chicago
Gokulnath Bhagavantha Rao , University of Illinois at Chicago
The model for data mining on streaming data assumes that there is a buffer of fixed length and a data stream of infinite length and the challenge is to extract patterns, changes, anomalies, and statistically significant structures by examining the data one time and storing records and derived attributes of length less than N. As data grids, data webs, and semantic webs become more common, mining distributed streaming data will become more and more important. The first step when presented with two or more distributed streams is to merge them using a common key. In this paper, we present two algorithms for merging streaming data using a common key. We also present experimental studies showing these algorithms scale in practice to OC-12 networks.

R. L. Grossman, J. Levera, A. Ananthanarayan, M. Mazzucco and G. B. Rao, "Merging Multiple Data Streams on Common Keys over High Performance Networks," SC Conference(SC), Baltimore, Maryland, 2002, pp. 67.
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