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Adaptive multi-stage distance join processing
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Source International Conference on Management of Data archive
Proceedings of the 2000 ACM SIGMOD international conference on Management of data table of contents
Dallas, Texas, United States
Pages: 343 - 354  
Year of Publication: 2000
ISBN:1-58113-217-4
Also published in ...
Authors
Hyoseop Shin  School of Computer Engr, Seoul National University, Seoul, Korea
Bongki Moon  Dept. of Computer Science, University of Arizona, Tucson, AZ
Sukho Lee  School of Computer Engr, Seoul National University, Seoul, Korea
Sponsor
SIGMOD: ACM Special Interest Group on Management of Data
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 2,   Downloads (12 Months): 34,   Citation Count: 11
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ABSTRACT

A spatial distance join is a relatively new type of operation introduced for spatial and multimedia database applications. Additional requirements for ranking and stopping cardinality are often combined with the spatial distance join in on-line query processing or internet search environments. These requirements pose new challenges as well as opportunities for more efficient processing of spatial distance join queries. In this paper, we first present an efficient k-distance join algorithm that uses spatial indexes such as R-trees. Bi-directional node expansion and plane-sweeping techniques are used for fast pruning of distant pairs, and the plane-sweeping is further optimized by novel strategies for selecting a sweeping axis and direction. Furthermore, we propose adaptive multi-stage algorithms for k-distance join and incremental distance join operations. Our performance study shows that the proposed adaptive multi-stage algorithms outperform previous work by up to an order of magnitude for both k-distance join and incremental distance join queries, under various operational conditions.


REFERENCES

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Bureau of the Census. Tiger/Line Precensus Files: 1997 technical documentation. Washington, DC, 1997.
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CITED BY  11
 
 
 
 
 
 
 
 

Collaborative Colleagues:
Hyoseop Shin: colleagues
Bongki Moon: colleagues
Sukho Lee: colleagues

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