Declustering using fractals

  • Authors:
  • Christos Faloutsos;Pravin Bhagwat

  • Affiliations:
  • -;-

  • Venue:
  • PDIS '93 Proceedings of the second international conference on Parallel and distributed information systems
  • Year:
  • 1993

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Abstract

We propose a method to achieve declustering for cartesian product files on M units. The focus is on range queries, as opposed to partial match queries that older declustering methods have examined. Our method uses a distance-preserving mapping, namely, the Hilbert curve, to impose a linear ordering on the multidimensional points (buckets); then, it traverses the buckets according to this ordering, assigning buckets to disks in a round-robin fashion. Thanks to the good distance-preserving properties of the Hilbert curve, the end result is that each disk contains buckets that are far away in the K-d address space. This is exactly the goal of declustering. Experiments show that these intuitive arguments lead indeed to good performance: the proposed method performs at least as well or better than older declustering schemes.