ACM SIGMOD Anthology ACM SIGMOD dblp.uni-trier.de

Fast Subsequence Matching in Time-Series Databases.

Christos Faloutsos, M. Ranganathan, Yannis Manolopoulos: Fast Subsequence Matching in Time-Series Databases. SIGMOD Conference 1994: 419-429
@inproceedings{DBLP:conf/sigmod/FaloutsosRM94,
  author    = {Christos Faloutsos and
               M. Ranganathan and
               Yannis Manolopoulos},
  editor    = {Richard T. Snodgrass and
               Marianne Winslett},
  title     = {Fast Subsequence Matching in Time-Series Databases},
  booktitle = {Proceedings of the 1994 ACM SIGMOD International Conference on
               Management of Data, Minneapolis, Minnesota, May 24-27, 1994},
  publisher = {ACM Press},
  year      = {1994},
  pages     = {419-429},
  ee        = {http://doi.acm.org/10.1145/191839.191925, db/conf/sigmod/FaloutsosRM94.html},
  crossref  = {DBLP:conf/sigmod/94},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

Abstract

We present an efficient indexing method to locate 1-dimensional subsequences within a collection of sequences, such that the subsequences match a given (query) pattern within a specified tolerance. The idea is to map each data sequence into a small set of multidimensional rectangles in feature space. Then, these rectangles can be readily indexed using traditional spatial access methods, like the R*-tree [Beckmann et al, SIGMOD 90]. In more detail, we use a sliding window over the data sequence and extract its features; the result is a trail in feature space. We propose an efficient and effective algorithm to divide such trails into sub-trails, which are subsequently represented by their Minimum Bounding Rectangles (MBRs). We also examine queries of varying lengths, and we show how to handle each case efficiently. We implemented our method and carried out experiments on synthetic and real data (stock price movements). We compared the method to sequential scanning, which is the only obvious competitor. The results were excellent: our method accelerated the search time from 3 times up to 100 times.

Copyright © 1994 by the ACM, Inc., used by permission. Permission to make digital or hard copies is granted provided that copies are not made or distributed for profit or direct commercial advantage, and that copies show this notice on the first page or initial screen of a display along with the full citation.


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Richard T. Snodgrass, Marianne Winslett (Eds.): Proceedings of the 1994 ACM SIGMOD International Conference on Management of Data, Minneapolis, Minnesota, May 24-27, 1994. ACM Press 1994 CiteSeerX Google scholar pubzone.org BibTeX bibliographical record in XML, SIGMOD Record 23(2), June 1994
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