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Self-Adaptive, On-Line Reclustering of Complex Object Data.

William J. McIver Jr., Roger King: Self-Adaptive, On-Line Reclustering of Complex Object Data. SIGMOD Conference 1994: 407-418
@inproceedings{DBLP:conf/sigmod/McIverK94,
  author    = {William J. McIver Jr. and
               Roger King},
  editor    = {Richard T. Snodgrass and
               Marianne Winslett},
  title     = {Self-Adaptive, On-Line Reclustering of Complex Object Data},
  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     = {407-418},
  ee        = {http://doi.acm.org/10.1145/191839.191924, db/conf/sigmod/McIverK94.html},
  crossref  = {DBLP:conf/sigmod/94},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}
BibTeX

Abstract

A likely trend in the development of future CAD, CASE and office information systems will be the use of object-oriented database systems to manage their internal data stores. The entities that these applications will retrieve, such as electronic parts and their connections or customer service records, are typically large complex objects composed of many interconnected heterogenous objects, not thousands of tuples. These applications may exhibit widely shifting usage patterns due to their interactive mode of operation. Such a class of applications would demand clustering methods that are appropriate for clustering large complex objects and that can adapt on-line to the shifting usage patterns. While most object-oriented clustering methods allow grouping of heterogenous objects, they are usually static and can only be changed off-line. We present one possible architecture for performing complex object reclustering in an on-line manner that is adaptive to changing usage patterns. Our architecture involves the decomposition of a clustering method into concurrently operating components that each handle one of the fundamental tasksinvolved in reclustering, namely statistics collection, cluster analysis, and reorganization. We present results of an experiment performed to evaluate its behavior. These results show that the average miss rate for object accesses can be effectively reduced using a combination of rules that we have developed for deciding when cluster analyses and reorganizations should be performed.

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 BibTeX , SIGMOD Record 23(2), June 1994
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References

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Referenced by

  1. Mohana Krishna Lakhamraju, Rajeev Rastogi, S. Seshadri, S. Sudarshan: On-line Reorganization in Object Databases. SIGMOD Conference 2000: 58-69
  2. Jérôme Darmont, Bertrand Petit, Michel Schneider: OCB: A Generic Benchmark to Evaluate the Performances of Object-Oriented Database Systems. EDBT 1998: 326-340
  3. Jonathan E. Cook, Artur Klauser, Alexander L. Wolf, Benjamin G. Zorn: Semi-automatic, Self-adaptive Control of Garbage Collection Rates in Object Databases. SIGMOD Conference 1996: 377-388
BibTeX
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