Parallel Algorithms for High-dimensional Similarity Joins for Data Mining Applications.

John C. Shafer, Rakesh Agrawal: Parallel Algorithms for High-dimensional Similarity Joins for Data Mining Applications. VLDB 1997: 176-185

@inproceedings{DBLP:conf/vldb/ShaferA97,
  author    = {John C. Shafer and
               Rakesh Agrawal},
  editor    = {Matthias Jarke and
               Michael J. Carey and
               Klaus R. Dittrich and
               Frederick H. Lochovsky and
               Pericles Loucopoulos and
               Manfred A. Jeusfeld},
  title     = {Parallel Algorithms for High-dimensional Similarity Joins for
               Data Mining Applications},
  booktitle = {VLDB'97, Proceedings of 23rd International Conference on Very
               Large Data Bases, August 25-29, 1997, Athens, Greece},
  publisher = {Morgan Kaufmann},
  year      = {1997},
  isbn      = {1-55860-470-7},
  pages     = {176-185},
  ee        = {db/conf/vldb/ShaferA97.html},
  crossref  = {DBLP:conf/vldb/97},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

Abstract

We consider the problem of parallelizing high-dimensional proximity joins. We present a parallel multidimensional join algorithm based on an epsilon-kdB tree abd compare it with the more common approach of space partitioning. An evaluation of the algorithm on an IBM SP2 shared-nothing multiprocessor is presented using both synthetic and real-life datasets. We also examine the effictiveness of the algorithms in the context of a specific data-mining problem, that of finding similar time-series. The empirical results show that our algorithm exhibits good performance and scalability, as well as ability to handle data-skew.

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Matthias Jarke, Michael J. Carey, Klaus R. Dittrich, Frederick H. Lochovsky, Pericles Loucopoulos, Manfred A. Jeusfeld (Eds.): VLDB'97, Proceedings of 23rd International Conference on Very Large Data Bases, August 25-29, 1997, Athens, Greece. Morgan Kaufmann 1997, ISBN 1-55860-470-7
Contents

References

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