International Journal of Communication Networks and Security IJCNS

ISSN: 2231-1882

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ijcct journal

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IIMT Bhubaneswar

IJCNS

NEW DYNAMIC QUERY OPTIMIZATION TECHNIQUE IN RELATIONAL DATABASE MANAGEMENT SYSTEMS


N. SATYANARAYANA
Dept CSE/IT LITS Khammam, A. P INDIA

SK. SHARFUDDIN
Dept CSE/IT LITS Khammam, A. P INDIA

SK.JAN BHASHA
Dept CSE/IT LITS Khammam, A. P INDIA


Abstract

Query optimizer is an important component in the architecture of relational data base management system. This component is responsible for translating user submitted query into an efficient query evolution program which can be executed against the database. The present query evolution existing algorithm tries to find the best possible plan to execute a query with a minimum amount of time using mostly semi accurate statistical information (e.g. sizes of temporary relations, selectivity factors, and availability of resources). It is a static approach for generating optimal or close to optimal execution plan. Which in turn increases the execution cost of the query to reduce the execution cost of the query; I propose a new dynamic query optimization algorithm which is based on greedy dynamic programming algorithm uses randomized strategies and reduces the execution cost of the queries and system resources and also it works efficiently with distributed and centralized databases.

Recommended Citation

[1]. Evolution of Query Optimization Methods by Abdelkader Hameurlain and Franck Morvan,http://www.csd.uoc.gr/~hy460/pdf/EvolutionofQue ryOptimizationMethods.pdf [2]. Alpdemir, M.N., Mukherjee, A., Gounaris, A., Paton, N.W., Fernandes, A.A.A., Sakellariou, R., Watson, P., Li, P.: Using OGSA-DQP to support scientific applications for the grid. In: Herrero, P., S. Pérez, M., Robles, V. (eds.) SAG 2004. LNCS, vol. 3458, pp. 13–24. Springer, Heidelberg (2005) [3]. Antonioletti, M., et al.: The design and implementation of Grid database services in OGSA-DAI. In: Concurrency and Computation: Practice & Experience, vol. 17, pp. 357–376. Wiley InterScience, Hoboken (2005) Arcangeli, J.-P., Hameurlain, A., Migeon, F., Morvan, F.: Mobile Agent Based Self-Adaptive Join for Wide-Area Distributed Query Processing. Jour. of Database Management 15(4), 25–44 (2004). [4]. Avnur, R., Hellerstein, J.-M.: Eddies: Continuously Adaptive Query Processing. In: Proc. of the ACM SIGMOD Intl. Conf. on Management of Data, vol. 29, pp. 261–272. ACM Press, New York (2000). [5]. Babu, S., Bizarro, P., De Witt, D.J.: Proactive reoptimization. In: Proc. of the ACM SIGMOD Intl. Conf. on Management of Data, pp. 107–118. ACM Press, New York (2005).[6] Bizarro, P., Bruno, N., De Witt, D.J.: Progressive Parametric Query Optimization. IEEE Transactions on Knowledge and Data Engineering 21(4), 582–594 (2009). [7]. Bose, S.K., Krishnamoorthy, S., Ranade, N.: Allocating Resources to Parallel Query Plans in Data Grids. In: Proc. of the 6th Intl. Conf. on Grid and Cooperative Computing, pp. 210–220. IEEE CS, Los Alamitos (2007) [8]. Bratbergsengen, K.: Hashing Methods and Relational Algebra Operations. In: Proc. of [9]. Bruno, N., Chaudhuri, S.: Efficient Creation of Statistics over Query Expressions. In: Proc. of the 19th Intl. Conf. on Data Engineering, Bangalore, India, pp. 201–212. IEEE CS, Los Alamitos (2003). 

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