Parallel and distributed search for structure in multivariate time series

Tim Oates, Matthew D. Schmill, Paul R. Cohen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Efficient data mining algorithms are crucial for effective knowledge discovery. We present the Multi-Stream Dependency Detection (MSDD) data mining algorithm that performs a systematic search for structure in multivariate time series of categorical data. The systematicity of MSDD's search makes implementation of both parallel and distributed versions straightforward. Distributing the search for structure over multiple processors or networked machines makes mining of large numbers of databases or very large databases feasible. We present results showing that MSDD efficiently finds complex structure in multivariate time series, and that the distributed version finds the same structure in approximately 1in of the time required by MSDD, where n is the number of machines across which the search is distributed.

Original languageEnglish (US)
Title of host publicationMachine Learning
Subtitle of host publicationECML-97 - 9th European Conference on Machine Learning, Proceedings
EditorsMaarten van Someren, Gerhard Widmer, Gerhard Widmer
PublisherSpringer-Verlag
Pages191-198
Number of pages8
ISBN (Print)3540628584, 9783540628583
DOIs
StatePublished - Jan 1 1997
Event9th European Conference on Machine Learning, ECML 1997 - Prague, Czech Republic
Duration: Apr 23 1997Apr 25 1997

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1224
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other9th European Conference on Machine Learning, ECML 1997
CountryCzech Republic
CityPrague
Period4/23/974/25/97

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Oates, T., Schmill, M. D., & Cohen, P. R. (1997). Parallel and distributed search for structure in multivariate time series. In M. van Someren, G. Widmer, & G. Widmer (Eds.), Machine Learning: ECML-97 - 9th European Conference on Machine Learning, Proceedings (pp. 191-198). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1224). Springer-Verlag. https://doi.org/10.1007/3-540-62858-4_84