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Integer Linear Programming Solution for the Multiple Query Optimization Problem

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Information Sciences and Systems 2014

Abstract

Multiple Query Optimization (MQO) is a technique for processing a batch of queries in such a way that shared tasks in these queries are executed only once, resulting in significant savings in the total evaluation. The first phase of MQO requires producing alternative query execution plans so that the shared tasks between queries are identified and maximized. The second phase of MQO is an optimization problem where the goal is selecting exactly one of the alternative plans for each query to minimize the total execution cost of all queries. A-star, branch-and-bound, dynamic programming (DP), and genetic algorithm (GA) solutions for MQO have been given in the literature. However, the performance of optimal algorithms, A-star and DP, is not sufficient for solving large MQO problems involving large number of queries. In this study, we propose an Integer Linear Programming (ILP) formulation to solve the MQO problem exactly for a large number of queries and evaluate its performance. Our results show that ILP outperforms the existing A-star algorithm.

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References

  1. E. Angel, E. Bampis, L. Gourvès, On the minimum hitting set of bundles problem, Algorithmic Aspects in Information and Management (Springer, Berlin, 2008), pp. 3–14

    Chapter  Google Scholar 

  2. M.A. Bayir, I.H. Toroslu, A. Cosar, Genetic algorithm for the multiple-query optimization problem. Syst. Man Cybern. Part C: Appl. Rev. IEEE Trans. 37(1), 147–153 (2007)

    Article  Google Scholar 

  3. J.R. Bernardino, P.S. Furtado, H.C. Madeira, Approximate query answering using data warehouse striping. J. Intell. Inf. Syst. 19(2), 145–167 (2002)

    Article  Google Scholar 

  4. F.C. Chen, M.H. Dunham, Common subexpression processing in multiple-query processing. Knowl. Data Eng. IEEE Trans. 10(3), 493–499 (1998)

    Article  Google Scholar 

  5. A. Cosar, E.P. Lim, J. Srivastava, Multiple query optimization with depth-first branch-and-bound and dynamic query ordering. in Proceedings of the second international conference on Information and knowledge management (ACM, 1993), (pp. 433–438)

    Google Scholar 

  6. A. Cosar, J. Srivastava, S. Shekhar, On the multiple pattern multiple object (MPMO) match problem. Army High Perform. Comput. Res. Cent. (1991)

    Google Scholar 

  7. A.A. Diwan, S. Sudarshan, D. Thomas, Scheduling and caching in multi-query optimization. In Proceedings of 13th International Conference Management of Data (2006)

    Google Scholar 

  8. S. Finkelstein, Common expression analysis in database applications. in Proceedings of the 1982 ACM SIGMOD international conference on Management of data (ACM, 1982), pp. 235–245

    Google Scholar 

  9. F. Polat, A. Cosar, R. Alhajj, Semantic information-based alternative plan generation for multiple query optimization. Inform. Sci. 137(1), 103–133 (2001)

    Article  MATH  Google Scholar 

  10. H.H. Lee, W.S. Lee, Adaptive two-level optimization for selection predicates of multiple continuous queries. J. Intell. Inform. Syst. 39(2), 317–334 (2012)

    Article  Google Scholar 

  11. R. Lee, M. Zhou, H. Liao, Request Window: an approach to improve throughput of RDBMS-based data integration system by utilizing data sharing across concurrent distributed queries. in Proceedings of the 33rd international conference on VLDB (2007), pp. 1219–1230

    Google Scholar 

  12. X. Lin, M. Orlowska, An Integer Linear Programming approach to data allocation with the minimum total communication cost in distributed database systems. Inform. Sci. 85(1), 1–10 (1995)

    Article  MathSciNet  Google Scholar 

  13. G. Nan, M. Li, Energy-efficient query management scheme for a wireless sensor database system. EURASIP J. Wirel. Commun. Netw. (2010)

    Google Scholar 

  14. S.G. Nash, A. Sofer, Linear and Nonlinear Programming, vol. 692 (McGraw-Hill, New York, 1996)

    Google Scholar 

  15. S. Papadomanolakis, A. Ailamaki, An integer linear programming approach to database design. in Data Engineering Workshop, 2007 IEEE 23rd International Conference on (2007), pp. 442–449

    Google Scholar 

  16. J. Rao, K.A. Ross, Reusing invariants: a new strategy for correlated queries. in ACM SIGMOD Record, vol. 27, No. 2, (1998), pp. 37–48

    Google Scholar 

  17. A. Rosenthal, U.S. Chakravarthy, Anatomy of a Modular Multiple Query Optimizer. In VLDB (1988), pp. 230–239

    Google Scholar 

  18. P. Roy, S. Seshadri, S. Sudarshan, S. Bhobe, Efficient and extensible algorithms for multi query optimization. in ACM SIGMOD Record, vol. 29, No. 2, (2000), pp. 249–260

    Google Scholar 

  19. T.K. Sellis, Multiple-query optimization. ACM Trans. Database Syst. (TODS) 13(1), 23–52 (1988)

    Article  Google Scholar 

  20. I.H. Toroslu, A. Cosar, Dynamic programming solution for multiple query optimization problem. Inform. Process. Lett. 92(3), 149–155 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  21. E. Wong, K. Youssefi, Decomposition—a strategy for query processing. ACM Trans. Database Syst. (TODS) 1(3), 223–241 (1976)

    Article  Google Scholar 

  22. T. Dokeroglu, S.A. Sert, M.S. Cinar, Evolutionary multiobjective query workload optimization of Cloud data warehouses. Sci. World J. (2014)

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Engineering, Middle East Technical University, Ankara, Turkey

    Tansel Dokeroglu, Murat Ali Bayır & Ahmet Cosar

Authors
  1. Tansel Dokeroglu
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  2. Murat Ali Bayır
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  3. Ahmet Cosar
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Corresponding author

Correspondence to Tansel Dokeroglu .

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Editors and Affiliations

  1. Polish Academy of Sciences, Gliwice, Poland

    Tadeusz Czachórski

  2. Imperial College London, London, United Kingdom

    Erol Gelenbe

  3. Imperial College London, London, United Kingdom

    Ricardo Lent

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© 2014 Springer International Publishing Switzerland

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Dokeroglu, T., Bayır, M.A., Cosar, A. (2014). Integer Linear Programming Solution for the Multiple Query Optimization Problem. In: Czachórski, T., Gelenbe, E., Lent, R. (eds) Information Sciences and Systems 2014. Springer, Cham. https://doi.org/10.1007/978-3-319-09465-6_6

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  • DOI: https://doi.org/10.1007/978-3-319-09465-6_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09464-9

  • Online ISBN: 978-3-319-09465-6

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