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Multi-step processing of spatial joins
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Source International Conference on Management of Data archive
Proceedings of the 1994 ACM SIGMOD international conference on Management of data table of contents
Minneapolis, Minnesota, United States
Pages: 197 - 208  
Year of Publication: 1994
ISBN:0-89791-639-5
Also published in ...
Authors
Thomas Brinkhoff  Institute for Computer Science, University of Munich, Leopoldstr. 11 B, D-80802 München, Germany
Hans-Peter Kriegel  Institute for Computer Science, University of Munich, Leopoldstr. 11 B, D-80802 München, Germany
Ralf Schneider  Institute for Computer Science, University of Munich, Leopoldstr. 11 B, D-80802 München, Germany
Bernhard Seeger  Institute for Computer Science, University of Munich, Leopoldstr. 11 B, D-80802 München, Germany
Sponsors
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGART: ACM Special Interest Group on Artificial Intelligence
SIGMOD: ACM Special Interest Group on Management of Data
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 11,   Downloads (12 Months): 56,   Citation Count: 52
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ABSTRACT

Spatial joins are one of the most important operations for combining spatial objects of several relations. In this paper, spatial join processing is studied in detail for extended spatial objects in two-dimensional data space. We present an approach for spatial join processing that is based on three steps. First, a spatial join is performed on the minimum bounding rectangles of the objects returning a set of candidates. Various approaches for accelerating this step of join processing have been examined at the last year's conference [BKS 93a]. In this paper, we focus on the problem how to compute the answers from the set of candidate which is handled by the following two steps. First of all, sophisticated approximations are used to identify answers as well as to filter out false hits from the set of candidates. For this purpose, we investigate various types of conservative and progressive approximations. In the last step, the exact geometry of the remaining candidates has to be tested against the join predicate. The time required for computing spatial join predicates can essentially be reduced when objects are adequately organized in main memory. In our approach, objects are first decomposed into simple components which are exclusively organized by a main-memory resident spatial data structure. Overall, we present a complete approach of spatial join processing on complex spatial objects. The performance of the individual steps of our approach is evaluated with data sets from real cartographic applications. The results show that our approach reduces the total execution time of the spatial join by factors.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
AA 83
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BK 94
Brinkhoff T., Kriegel H.-P.: 'The Impact of Global Clusterin8 on Spatial Database Systems ', Technical report, University of Munich, 1994.
BKS 93a
 
BKS 93b
BKSS 90
BZ 91
 
DB 83
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Fal 88
 
Gün 89
 
Gün 93
Gut 84
 
Jag 90a
Jag 90b
 
KBS 93
Kriegel H.-P., Brinkhoff T., Schneider R.: 'Ej#%ient Spatial Query Processing in Geographic Database Systems', IEEE Data Engineering Bulletin, Vol. 16, No. 3, 1993, pp. 10-15.
 
KHS 91
ME 92
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PS 85
 
Sam 90
 
SH 76
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Wel91
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CITED BY  52

Collaborative Colleagues:
Thomas Brinkhoff: colleagues
Hans-Peter Kriegel: colleagues
Ralf Schneider: colleagues
Bernhard Seeger: colleagues