Evaluating Query Execution Plans by Implementing Join Operators using Particle Swarm Optimization

Sambit Kumar Mishra^1, , Srikanta Pattnaik² and Dulu Patnaik^3,

¹Department of Computer Sc.&Engg, Ajay Binay Institute of Technology, Cuttack, Odisha, India

²S.O.A. University, Bhubaneswar, Odisha, India

³Government College of Engineering, Bhwanipatna, Odisha, India

Cite this paper:
Sambit Kumar Mishra, Srikanta Pattnaik and Dulu Patnaik. Evaluating Query Execution Plans by Implementing Join Operators using Particle Swarm Optimization. Journal of Computer Sciences and Applications. 2014; 2(2):31-35. doi: 10.12691/jcsa-2-2-3

Abstract

The nested structured queries as well as nested iteration as operator in both the logical and physical query algebra have been sometimes neglected in research. Interesting issues arise if multiple invocations of the same nested computation affect each other, e.g., the first invocation warms up the I/O buffer for subsequent ones. Other interesting issues arise if different nested computations compete for resources, e.g., I/O buffer or memory for sort and hash operations within inner queries. Nested computations are very important in practice, both because queries are authored using nested structured queries and because nested iteration based on index-to-index navigation often is the best execution plan. Therefore, nested computations could be a very fruitful research topic, both execution and optimization, and could probably also benefit from more dynamic and adaptive techniques than those in use today. While most resource issues have relatively little impact on optimal plan choices (even if they affect the ranking among different plans of fairly similar costs), one issue that is crucial in practice but usually ignored in academic research is the effect of buffer hits and faults in complex query plans. However, a conceptual model may be needed of nested queries that are substantially simpler, e.g., based on algebra expressions with a table of parameter values. In this paper it is aimed to find location of local minima of particle, random velocities of particles considering the relation schemes. The query plans related to relation schemes may be represented as particles. The query is optimized at compile time by that the complete query execution plans may be generated. The function evaluation of particles represented in terms of query plans in the relation schemes is planned to be done by considering random population of continuous values and velocities.

Keywords:
query plan OLAP OLTP tuple swarm Query Scrambling

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References:

[1]	Kristina Zelenay, “Query Optimization”, ETH Zürich, Seminar Algorithmen für Datenbanksysteme, June 2005
[2]	S. R. Madden, M. A. Shah, J. M. Hellerstein, and V. Raman. Continuously adaptive continuous queries over streams. in Proc. SIGMOD, 2002.
[3]	R. Avnur and J. M. Hellerstein, Eddies: Continuously Adaptive Query Optimization, SIGMOD 2000.
[4]	P.G. Selinger et al. Access Path Selection in a Relational DBMS. SIGMOD 1979.
[5]	Ganguly, S., Hasan, W., Krishnamurthy, R.: Query Optimization for Parallel Execution. In: Proc. of the 1992 ACM SIGMOD int’l. Conf. on Management of Data, vol. 21, pp. 9-18. ACM Press, San Diego (1992).
[6]	J. Chen, D. J. DeWitt, F. Tian, and Y. Wang. NiagaraCQ: a scalable continuous query system for internet databases.In SIGMOD, p. 379-390. ACM, 2000.
[7]	A. Halevy, Z. Ives, J. Madhavan, P. Mork, D. Suciu, and I. Tatarinov. The piazza peer data management system. IEEE Trans. Knowl. Data Eng., 16(7):787-798, 2004.
[8]	F. Neven and D. Van de Craen. Optimizing monitoring queries over distributed data. In EDBT, p. 829-846, 2006.
[9]	J. Bleiholder, et al. Query planning in the presence of overlapping sources. In EDBT, p. 811-828, 2006.
[10]	G. M. Thomas Neumann. Analysis of two existing and one new dynamic programming algorithm for the eneration of optimal bushy join trees without cross products. In Proceedings of the 32nd international conference on Very large data bases, pages 930-941. VLDB Endowment, 2008.
[11]	T. Neumann. Query simplification: Graceful degradation for join-order optimization. In C. Binning and B. Dageville, editors, SIGMOD-PODS09: Compilation Proceedings of the International Conference on Management of Data 28th Symposium on Principles of Database Systems, pages 403-414, Providence, USA, June 2009. Association for Computing Machinery (ACM).
[12]	AlinDeutsch, Bertram Ludaascher, and Alan Nash. Rewriting queries using views with access patterns under in tegrit y constraints. The or.Comput.Sci, 371(3):200-226, 2007.
[13]	G. Tan, N. Sun, and G. R. Gao. A parallel dynamic programming algorithm on a multi-core architecture. In SPAA, 2007.
[14]	G. Moerkotte and T. Neumann. Dynamic programming strikes back. In SIGMOD, 2008.
[15]	W.-S. Han, W. Kwak, J. Lee, G. M. Lohman, and V. Markl. Parallelizing query optimization. In VLDB, 2008.
[16]	N. W. Paton, V. Raman, G. Swart, and I. Narang. Autonomic query parallelization using non-dedicated computers: An evaluation of adaptivity options. In ICAC, 2006.