Distributing a Search Tree Among a Growing Number of Processors.

Brigitte Kröll, Peter Widmayer: Distributing a Search Tree Among a Growing Number of Processors. SIGMOD Conference 1994: 265-276

@inproceedings{DBLP:conf/sigmod/KrollW94,
  author    = {Brigitte Kr{\"o}ll and
               Peter Widmayer},
  editor    = {Richard T. Snodgrass and
               Marianne Winslett},
  title     = {Distributing a Search Tree Among a Growing Number of Processors},
  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     = {265-276},
  ee        = {http://doi.acm.org/10.1145/191839.191891, db/conf/sigmod/KrollW94.html},
  crossref  = {DBLP:conf/sigmod/94},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

BibTeX

Abstract

Databases are growing steadily, and distributed computer systems are more and more easily available. This provides an opportunity to satisfy the increasingly tighter efficiency requirements by means of distributed data structures. The design and analysis of these structures under efficiency aspects, however, has not yet been studied sufficiently. To our knowledge, a single scalable, distributed data structure has been proposed so far. It is a distributed variant of linear hashing with uncontrolled splits, and, as a consequence, performs efficiently for data distributions that are close to uniform, but not necessarily for others. In addition, it does not support queries that refer to the linear order of keys, such as nearest neighbor or range queries. We propose a distributed search tree that avoids these problems, since it inherits desirable properties from non-distributed trees. Our experiments show that our structure does indeed combine a guarantee for good storage space utilization with high query efficiency. Nevertheless, we feel that further research in the area of scalable, distributed data structures is dearly needed; it should eventually lead to a body of knowledge that is comparable with the non-distributed, classical data structures field.

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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BibTeX
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Referenced by

Mong-Li Lee, Masaru Kitsuregawa, Beng Chin Ooi, Kian-Lee Tan, Anirban Mondal: Towards Self-Tuning Data Placement in Parallel Database Systems. SIGMOD Conference 2000: 225-236
Junping Sun, William I. Grosky: Dynamic Maintenance of Multidimensional Range Data Partitioning for Parallel Data Processing. DOLAP 1998: 72-79
André Eickler, Alfons Kemper, Donald Kossmann: Finding Data in the Neighborhood. VLDB 1997: 336-345
Witold Litwin, Marie-Anne Neimat, Donovan A. Schneider: LH* - A Scalable, Distributed Data Structure. ACM Trans. Database Syst. 21(4): 480-525(1996)
Jonas S. Karlsson, Witold Litwin, Tore Risch: LH*LH: A scalable High Performance Data Structure for Switched Multicomputers. EDBT 1996: 573-591
Gerhard Weikum: Tutorial on Parallel Database Systems. ICDT 1995: 33-37
Witold Litwin, Marie-Anne Neimat, Donovan A. Schneider: RP*: A Family of Order Preserving Scalable Distributed Data Structures. VLDB 1994: 342-353

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