American Journal of Information Systems
ISSN (Print): 2374-1953 ISSN (Online): 2374-1988 Website: http://www.sciepub.com/journal/ajis Editor-in-chief: Sergii Kavun
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American Journal of Information Systems. 2014, 2(3), 52-55
DOI: 10.12691/ajis-2-3-2
Open AccessArticle

Estimating Plans along with Cost in Multiple Query Processing Environments by Applying Particle Swarm Optimization Technique

Sambit Kumar Mishra1, , Srikanta Pattnaik2 and Dulu patnaik3

1Deaprtment of Computer Sc.&Engg., Ajay Binay Institute of Technology, Cuttack

2S.O.A. University, Bhubaneswar

3Government College of Engineering, Bhawanipatna

Pub. Date: December 29, 2014

Cite this paper:
Sambit Kumar Mishra, Srikanta Pattnaik and Dulu patnaik. Estimating Plans along with Cost in Multiple Query Processing Environments by Applying Particle Swarm Optimization Technique. American Journal of Information Systems. 2014; 2(3):52-55. doi: 10.12691/ajis-2-3-2

Abstract

The Main idea of multiple query processing is to optimize a set of queries together and execute the common operations once. Major tasks in multiple query processing are common operation or expression identification and global execution plan construction. Query plans are generally derived from registered continuous queries. They are composed of operators, which perform the actual data processing, queries which buffer data as it moves between operators to hold state of operators. The complex part is to decompose queries and query plans and rearrange the sub queries and query plans on the network. The main functions to achieve an optimal query distribution are usually minimizing network usage and minimizing response time of queries. While dealing with query distribution problem, the challenges like modeling topology of the network, decomposing queries into some sub queries and sub query placement may be occurred. Operators are the basic data processing units in a query plan. An operator takes one or more streams as input and produces a stream as output. As in the traditional database management system, a plan for query connects a set of operators in a tree. The output of a child operator forms an input of its parent operator. In this paper it is aimed to retrieve the cost of query plans as well as cost of particles of swarm in multiple query processing environments by applying particle swarm optimization techniques.

Keywords:
query plan swarm NP hard particle SMT personal best global best

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References:

[1]  Y.E. Ioannidis and Y.C. Kang, “Randomized algorithms for optimizing large join queries, ACM 1990.
 
[2]  M. Jarke and J. Koch, “Query optimization in database systems,” ACM Computing Surveys, volume 16, no. 2, pp. 111-152, June 1984.
 
[3]  L. P. Mahalingam and K. S. Candan, Multi-Criteria Query Optimization in the Presence of Result Size and Quality Tradeoffs, Multimedia Tools and Applications Journal 23(3) (2004), 167-183.
 
[4]  Ch. Li, Kevin Ch.-Ch. Chang, I. F. Ilyas, and S. Song, RankSQL: query algebra and optimization for relational top-k queries. In: F. Ozcan, editor, SIGMOD Conference. ACM, 2005, 131-142.
 
[5]  Stefan Riezler, Statistical Machine Translation for Query Expansion in Answer Retrieval, Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pages 464-471, Prague, Czech Republic, June 2007.
 
[6]  Raymond T. Ng and V. S. Subrahmanian. Probabilistic logic programming. Information and Computation, 101(2):150-201, 1992.
 
[7]  Norbert Fuhr and Thomas Rolleke. A probabilistic relational algebra for the integration of information retrieval and database systems. ACM Trans. Inf. Syst., 15(1):32-66, 1997.
 
[8]  Praveen Seshadri, Hamid Pirahesh, and T. Y. Cliff Leung. Complex query decorrelation. In Intl. Conf. on Data Engineering, 1996.
 
[9]  Subbu N. Subramanian and Shivakumar Venkataraman. Cost based optimization of decision support queries using transient views. In SIGMOD Intl. Conf. on Management of Data, Seattle, WA, 1998.
 
[10]  A. Pérez-Uribe and B. Hirsbrunner,―Learning and foraging in robot-bees‖, in Meyer, Berthoz, Floreano, Roitblat andWilson (eds.)’, SAB2000 Proceedings Supplement Book, Intermit. Soc. For Adaptive Behavior, Honolulu, Hawaii, pp. 185-194.
 
[11]  K. Bennett, M.C. Ferris, and Y.E. Ioannidis, ―A genetic algorithm for database query optimization‖, In Proc. of the 4th International Conference on Genetic Algorithms, 400-407, 1991.
 
[12]  M.J. Franklin, S.R. Jeffery, S. Krishnamurthy, F. Reiss, S. Rizvi, et al., ―Design considerations for high fan-in systems: the HiFi approach‖, In Proc. Of the CIDR Conf., Jan. 2005.
 
[13]  A. Sokolov and D. Whitley, “Unbiased Tournament Selection,” in proceedings of the 2005 conference on Genetic and Evolutionary Computation, pp. 1131-1138, 2005
 
[14]  T.V. VijayKumar, Vikram Singh and Ajay Kumar Verma, “Generating Distributed Query Processing Plans using Genetic Algorithm”, In the proceedings of the International Conference on Data Storage and Data Engineering (DSDE 2010), Bangalore, February 9-10, 2010, pp. 173-177, 2010.