Clustering Techniques for Minimizing External Path Length.

@inproceedings{DBLP:conf/vldb/DiwanRSS96,
  author    = {Ajit A. Diwan and
               Sanjeeva Rane and
               S. Seshadri and
               S. Sudarshan},
  editor    = {T. M. Vijayaraman and
               Alejandro P. Buchmann and
               C. Mohan and
               Nandlal L. Sarda},
  title     = {Clustering Techniques for Minimizing External Path Length},
  booktitle = {VLDB'96, Proceedings of 22th International Conference on Very
               Large Data Bases, September 3-6, 1996, Mumbai (Bombay), India},
  publisher = {Morgan Kaufmann},
  year      = {1996},
  isbn      = {1-55860-382-4},
  pages     = {342-353},
  ee        = {db/conf/vldb/DiwanRSS96.html},
  crossref  = {DBLP:conf/vldb/96},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

Abstract

There are a variety of main-memory access structures, such as segment trees, and quad trees, whose properties, such as good worst-case behaviour, make them attractive for database applications. Unfortunately, the structures are typically `long and skinny', whereas disk data structures must be `short-and-fat' (that is, have a high fanout and low height) in order to minimize I/O.

We consider how to cluster the nodes (that is, map the nodes to disk pages) of mainmemory access structures such that that although a path may traverse many nodes, it only traverses a few disk pages. The number of disk pages traversed in a path is called the external path length. We address several version of the clustering problem. We present a clustering algorithm for tree structures that generates optimal worst-case external path length mappings; we also show how to make it dynamic, to support updates. We extend the algorithm to generate mappings that minimize the average weighted external path lengths. We also show that some other clustering problems, such as finding optimal external path lengths for DAG structures and minimizing weights for optimal height mappings, are NP-complete. We present efficient heurisitcs for these problem.

We present a performance study (using quad-trees on actual image data as an example) which shows that our algorithms perform well. Our algorithms can also be used for clustering complex objects in object-oriented databases.

Copyright © 1996 by the VLDB Endowment. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by the permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, requires a fee and/or special permission from the Endowment.

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