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NSJ: an efficient non-blocking spatial join algorithm
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Source Geographic Information Systems archive
Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems table of contents
Arlington, Virginia, USA
SESSION: Stream processing & query processing II table of contents
Pages: 235 - 242  
Year of Publication: 2006
ISBN:1-59593-529-0
Authors
Guifen Tang  National University of Defense Technology, ChangSha, China
Jose E. Córcoles  Castilla-La Mancha University, Albacete, Spain
Ning Jing  National University of Defense Technology, ChangSha, China
Sponsors
ACM: Association for Computing Machinery
SIGIR: ACM Special Interest Group on Information Retrieval
Publisher
ACM  New York, NY, USA
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ABSTRACT

This paper introduces an efficient non-blocking spatial join (NSJ, for short) algorithm to deal with spatial objects from remote sources via underlying network. The objectives of NSJ are: (1) start reporting the first output join results as soon as possible, and (2) minimize the cost for output the remaining results. As some other previous non-blocking join algorithms, NSJ includes two stages: memory-join stage and disk-join stage The memory-join stage employs a join process as along as receiving spatial objects, and the disk-join stage is responsible for the uncompleted join process during the memory-join stage after all spatial objects are received completely. We propose a dynamic concurrent flush policy(DCFP) based on resident degree to process memory overflow, which makes join process in memory-join stage more efficiently. We also develop an optimal data access schedule algorithm based on BEA (Bond Energy Algorithm) to reduce redundant I/O and CPU cost in disk-join stage. Extensive experiments prove that our technique delivers result significantly more efficient than the previous methods.


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.

 
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Collaborative Colleagues:
Guifen Tang: colleagues
Jose E. Córcoles: colleagues
Ning Jing: colleagues