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Parallel R-trees.

Ibrahim Kamel, Christos Faloutsos: Parallel R-trees. SIGMOD Conference 1992: 195-204
@inproceedings{DBLP:conf/sigmod/KamelF92,
  author    = {Ibrahim Kamel and
               Christos Faloutsos},
  editor    = {Michael Stonebraker},
  title     = {Parallel R-trees},
  booktitle = {Proceedings of the 1992 ACM SIGMOD International Conference on
               Management of Data, San Diego, California, June 2-5, 1992},
  publisher = {ACM Press},
  year      = {1992},
  pages     = {195-204},
  ee        = {http://doi.acm.org/10.1145/130283.130315, db/conf/sigmod/KamelF92.html},
  crossref  = {DBLP:conf/sigmod/92},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}
BibTeX

Abstract

We consider the problem of exploiting parallelism to accelerate the performance of spatial access methods and specifically, R-trees [11]. Our goal is to design a server for spatial data, so that to maximize the throughput of range queries. This can be achieved by (a) maximizing parallelism for large range queries, and (b) by engaging as few disks as possible on point queries [22].

We propose a simple hardware architecture consisting of one processor with several disks attached to it. On this architecture, we propose to distribute the nodes of a traditional R-tree, with cross-disk pointers ('Multiplexed' R-tree). The R-tree code is identical to the one for a single-disk R-tree, with the only addition that we have to decide which disk a newly created R-tree node should be stored in. We propose and examine several criteria to choose a disk for a new node. The most successful one, termed 'proximity index' or PI, estimates the similarity of the new node with the other R-tree nodes already on a disk, and chooses the disk with the lowest similarity. Experimental results show that our scheme consistently outperforms all the other heuristics for node-to-disk assignments, achieving up to 55% gains over the Round Robin one. Experiments also indicate that the multiplexed R-tree with PI heuristic gives better response time than the disk-stripping (="Super-node") approach, and imposes lighter load on the I/O sub-system.

The speed up of our method is close to linear speed up, increasing with the size of the queries.

Copyright © 1992 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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Michael Stonebraker (Ed.): Proceedings of the 1992 ACM SIGMOD International Conference on Management of Data, San Diego, California, June 2-5, 1992. ACM Press 1992 BibTeX , SIGMOD Record 21(2), June 1992
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References

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Bernhard Seeger, Per-Åke Larson: Multi-Disk B-trees. SIGMOD Conference 1991: 436-445 BibTeX
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Referenced by

  1. Dimitris Papadias, Nikos Mamoulis, Yannis Theodoridis: Processing and Optimization of Multiway Spatial Joins Using R-Trees. PODS 1999: 44-55
  2. Botao Wang, Hiroyuki Horinokuchi, Kunihiko Kaneko, Akifumi Makinouchi: Parallel R-Tree Search Algorithm on DSVM. DASFAA 1999: 237-245
  3. Shashi Shekhar, Sivakumar Ravada, Vipin Kumar, Douglas Chubb, Greg Turner: Declustering and Load-Balancing Methods for Parallelizing Geographic Information Systems. IEEE Trans. Knowl. Data Eng. 10(4): 632-655(1998)
  4. Volker Gaede, Oliver Günther: Multidimensional Access Methods. ACM Comput. Surv. 30(2): 170-231(1998)
  5. Apostolos Papadopoulos, Yannis Manolopoulos: Similarity Query Processing Using Disk Arrays. SIGMOD Conference 1998: 225-236
  6. Chialin Chang, Bongki Moon, Anurag Acharya, Carter Shock, Alan Sussman, Joel H. Saltz: Titan: A High-Performance Remote Sensing Database. ICDE 1997: 375-384
  7. Peter Muth, Achim Kraiss, Gerhard Weikum: LoT: Dynamic Declustering of TSB-Tree Nodes for Parallel Access to Temporal Data. EDBT 1996: 553-572
  8. Nick Koudas, Christos Faloutsos, Ibrahim Kamel: Declustering Spatial Databases on a Multi-Computer Architecture. EDBT 1996: 592-614
  9. Gerhard Weikum: Tutorial on Parallel Database Systems. ICDT 1995: 33-37
  10. Duen-Ren Liu, Shashi Shekhar: A Similarity Graph-Based Approach to Declustering Problems and Its Application towards Paralleling Grid Files. ICDE 1995: 373-381
  11. M. G. Martynov: Spatial Joins and R-trees. ADBIS 1995: 295-304
  12. Maurício R. Mediano, Marco A. Casanova, Marcelo Dreux: V-Trees - A Storage Method for Long Vector Data. VLDB 1994: 321-330
  13. Erik G. Hoel, Hanan Samet: Performance of Data-Parallel Spatial Operations. VLDB 1994: 156-167
  14. Yvonne Zhou, Shashi Shekhar, Mark Coyle: Disk Allocation Methods for Parallelizing Grid Files. ICDE 1994: 243-252
  15. Vram Kouramajian, Ramez Elmasri, Anurag Chaudhry: Declustering Techniques for Parallelizing Temporal Access Structures. ICDE 1994: 232-242
  16. Christos Faloutsos, Ibrahim Kamel: High Performance R-trees. IEEE Data Eng. Bull. 16(3): 28-33(1993)
  17. Pavel Zezula, Paolo Ciaccia, Paolo Tiberio: Hamming Filters: A Dynamic Signature File Organization for Parallel Stores. VLDB 1993: 314-327
  18. Thomas Brinkhoff, Hans-Peter Kriegel, Bernhard Seeger: Efficient Processing of Spatial Joins Using R-Trees. SIGMOD Conference 1993: 237-246
BibTeX
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