Guifen Tang
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.
- M. F Mokbel, M. Lu, and W. G. Aref. Hash-merge Join: A non-blocking join algorithm for producing fast and early join results. In ICDE, pages 251--263, 2004. Google Scholar
Digital Library
- G. Luo, J. F. Naughton, and C. Ellmann. A non-blocking parallel spatial join algorithm. In ICDE 2002, pages 697--705. Google ScholarDigital Library
- P. J. Haas and J. M. Hellerstein. Ripple Joins for Online Aggregation. In Proc. SIGMOD, pages 287--298, 1999. Google ScholarDigital Library
- Z. G. Ives, D. Florescu, M. Friedman, A. Y. Levy, and D S Weld. An Adaptive Query Execution System for Data Integration. In Proc. SIGMOD, pages 299--310, 1999. Google ScholarDigital Library
- G. Luo, C. J. Ellmann, P. J. Haas and J. F. Naughton. A Scalable Hash Ripple Join Algorithm. In Proc. SIGMOD, pages 40--51, 2002. Google ScholarDigital Library
- A. N. Wilschut and P. M. G. Apers. Dataflow Query Execution in a Parallel Main-Memory Environment. In Proc. PDIS, pages 68--77, 1991. Google ScholarDigital Library
- T. Urhan and M. J. Franklin. XJoin: A reactively-scheduled pipelined join operator. In Proc. TKDE, 23(2):27--33, 2000.Google Scholar
- Y. Tao, M. Yiu and D. Papadias. RPJ: Producing Fast Join Results on Streams through Rate based Optimization. In Proc. SIGMOD, pages 371--382. 2005. Google ScholarDigital Library
- S. Viglas, J. F. Naughton and J. Burger. Maximizing the output rate of multi-way join queries over streaming information sources. In VLDB, pages 285--296, 2002. Google ScholarDigital Library
- J. P. Dittrich, B. Seeger, D. S. Taylor, and P. Widmayer. Progressive merge join: A generic and non-blocking sort-based join algorithm. In VLDB, pages 299--310, 2002. Google ScholarDigital Library
- J. Dittrich and B. Seeger. Data Redundancy and Duplicate Detection in Spatial Join Processing. In ICDE. 535--546, 2000. Google ScholarDigital Library
- J. M. Patel and D. J. DeWitt. Partition based spatial-merge join. In Proc. SIGMOD, pages 259--270. 1996. Google ScholarDigital Library
- X. P. Wang and L. M. Chao. Genetic Algorithm-Theory, application and implement. Xi'an JiaoTong university publication. 2004 the forth version.Google Scholar
- J. McCirmick, P. J. Schweitzer and T. W. White(1972). Problem decomposition by a clustering technique. Op. Res, 20, 993--1009.Google Scholar
- J. Xiao, Y. Zhang and X. Jia. Clustering non-uniform-sized spatial objects to reduce I/O cost for spatial-join processing. The Computer Journal, 2001, 44(5):384--397.Google ScholarCross Ref
- W. Xiong, W. Liao, F. Zhang, H. S. Chen and N. Jing. Genetic Optimization to the Refinement Step of Spatial Join Processing Journal of electron, 2006.Google Scholar
- US Bureau of the Census. TIGER/Line Files. http://www.census.gov.Google Scholar
- T. Urhan and M. J. Franklin. XJoin: Getting Fast Answers from Slow and Bursty Networks. University of Maryland Technical Report, CS-TR-3994., February, 1999.Google Scholar
- D. A. Schneider and D. J. DeWitt. A Performance Evaluation of Four Parallel Join Algorithms in a Shared-Nothing Multiprocessor Environment. In SIGMOD, pages 110--121, 1989. Google ScholarDigital Library
Index Terms
- NSJ: an efficient non-blocking spatial join algorithm
Recommendations
Efficient processing of spatial joins with DOT-based indexing
A spatial join is a query that searches for a set of object pairs satisfying a given spatial relationship from a database. It is one of the most costly queries, and thus requires an efficient processing algorithm that fully exploits the features of the ...
Grasping the gap between blocking and non-blocking transactional memories
Transactional memory (TM) is an inherently optimistic abstraction: it allows concurrent processes to execute sequences of shared-data accesses (transactions) speculatively, with an option of aborting them in the future. Early TM designs avoided using ...
Looking for efficient implementations of concurrent objects
PaCT'11: Proceedings of the 11th international conference on Parallel computing technologiesAs introduced by Taubenfeld, a contention-sensitive implementation of a concurrent object is an implementation such that the overhead introduced by locking is eliminated in the common cases, i.e., when there is no contention or when the operations ...
Comments