Abstract
The multiple query optimization problem (MQO) has been largely studied in traditional and advanced databases. In scientific and statistical databases, queries are complex, recurrent and share common sub-expressions. As a consequence, the MQO problem re-emerges in this context. An important characteristic of the MQO problem is that its result (usually represented by a unified plan merging isolated plans of the workload queries) may be used to select optimization structures (OS) such as materialized views. By examining the literature, we discover that the interconnection between these two problems is often neglected. Ignoring what-if questions about selecting the unified plan can result in disastrous consequences when used as a basis for selecting OS. In this paper, we first exhibit the link between global plans and optimization structures. Secondly, we give a formalization of the OS-oriented unified plan generation problem. Thirdly, a generic approach for plan generation is given. Finally, we instantiate our formalization to deal with the problems of selecting materialized views and horizontal data partitioning and we show its effectiveness and efficiency through intensive experiments.
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References
Agrawal, S., Chaudhuri, S., Narasayya, V.R.: Automated selection of materialized views and indexes in sql databases. In: Proceedings of the 26th International Conference on Very Large Data Bases, VLDB 2000, pp. 496–505. Morgan Kaufmann Publishers Inc., San Francisco (2000)
Baralis, E., Paraboschi, S., Teniente, E.: Materialized view selection in a multidimensional database. In: Proceedings of the International Conference on Very Large Databases (VLDB), pp. 156–165 (August 1997)
Bayir, M., Toroslu, I., Cosar, A.: Genetic algorithm for the multiple-query optimization problem. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 37, 147–153 (2007)
Bellatreche, L., Kerkad, A., Breß, S., Geniet, D.: RouPar: Routinely and mixed query-driven approach for data partitioning. In: Meersman, R., Panetto, H., Dillon, T., Eder, J., Bellahsene, Z., Ritter, N., De Leenheer, P., Dou, D. (eds.) ODBASE 2013. LNCS, vol. 8185, pp. 309–326. Springer, Heidelberg (2013)
Boukorca, A., Bellatreche, L., Senouci, S.-A.B., Faget, Z.: SONIC: Scalable multi-query optimizatioN through integrated circuits. In: Decker, H., Lhotská, L., Link, S., Basl, J., Tjoa, A.M. (eds.) DEXA 2013, Part I. LNCS, vol. 8055, pp. 278–292. Springer, Heidelberg (2013)
Chaudhuri, S., Narasayya, V.R.: Autoadmin ’what-if’ index analysis utility. In: Proc. ACM SIGMOD Int. Conf. on Management of Data, SIGMOD 1998, pp. 367–378. ACM Press (1998)
Finkelstein, S.: Common expression analysis in database applications. In: Proceedings of the 1982 ACM SIGMOD International Conference on Management of Data, SIGMOD 1982, pp. 235–245. ACM (1982)
Galindo-Legaria, C.A., Grabs, T., Gukal, S., Herbert, S., Surna, A., Wang, S., Yu, W., Zabback, P., Zhang, S.: Optimizing star join queries for data warehousing in microsoft sql server. In: Proceedings of the International Conference on Data Engineering (ICDE), pp. 1190–1199 (2008)
Goasdoué, F., Karanasos, K., Leblay, J., Manolescu, I.: View selection in semantic web databases. Proceedings of the VLDB Endowment 5(2), 97–108 (2011)
Golfarelli, M., Rizzi, S.: What-if simulation modeling in business intelligence. IJDWM 5(4), 24–43 (2009)
Gupta, H.: Selection and maintenance of views in a data warehouse. Ph.d. thesis, Stanford University (September 1999)
Herodotou, H., Babu, S.: Profiling, what-if analysis, and cost-based optimization of mapreduce programs. PVLDB 4(11), 1111–1122 (2011)
Ioannidis, Y.E., Kang, Y.C.: Randomized algorithms for optimizing large join queries. In: Garcia-Molina, H., Jagadish, H.V. (eds.) ACM SIGMOD, pp. 312–321 (1990)
Jarke, M., Koch, J.: Query optimization in database systems. ACM 16(2), 111–152 (1984)
Karypis, G., Aggarwal, R., Kumar, V., Shekhar, S.: Multilevel hypergraph partitioning: applications in vlsi domain. IEEE Transactions on Very Large Scale Integration Systems 7(1), 69–79 (1999)
Karypis, G., Kumar, V.: Multilevel k-way hypergraph partitioning. In: ACM/IEEE Design Automation Conference (DAC), pp. 343–348. ACM, New York (1999)
Kerkad, A., Bellatreche, L., Geniet, D.: Queen-bee: Query interaction-aware for buffer allocation and scheduling problem. In: Cuzzocrea, A., Dayal, U. (eds.) DaWaK 2012. LNCS, vol. 7448, pp. 156–167. Springer, Heidelberg (2012)
Kerkad, A., Bellatreche, L., Geniet, D.: La fragmentation horizontale revisitée: Prise en compte de l’interaction des requétes. In: 9èmes Journées Francophones sur les Entrepôts de Données et Analyse en Ligne, EDA 2013 (2013)
Le, W., Kementsietsidis, A., Duan, S., Li, F.: Scalable multi-query optimization for sparql. In: Proceedings of the International Conference on Data Engineering (ICDE), pp. 666–677. IEEE (2012)
O’Neil, P., O’Neil, B., Chen, X.: Star schema benchmark (2009)
Park, J., Segev, A.: Using common subexpressions to optimize multiple queries. In: Proceedings of the Fourth International Conference on Data Engineering, pp. 311–319 (1988)
Roy, P., Seshadri, S., Sudarshan, S., Bhobe, S.: Efficient and extensible algorithms for multi query optimization. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, pp. 249–260. ACM (2000)
Sellis, T., Ghosh, S.: On the multiple query optimization problem. IEEE Transaction on Knowledge and Data Engineering, 262–266 (1990)
Sellis, T.K.: Multiple-query optimization. ACM Transactions on Database Systems 13(1), 23–52 (1988)
Shim, K., Sellis, T., Nau, D.: Improvements on a heuristic algorithm for multiple-query optimization. Data, Knowledge Engineering, 197–222 (1994)
Toroslu, I.H., Cosar, A.: Dynamic programming solution for multiple query optimization problem. Information Processing Letters 92(3), 149–155 (2004)
Yang, J., Karlapalem, K., Li, Q.: Algorithms for materialized view design in data warehousing environment. In: Proceedings of the International Conference on Very Large Databases (VLDB), pp. 136–145. Morgan Kaufmann Publishers Inc., San Francisco (1997)
Yang, J., Karlapalem, K., Li, Q.: A framework for designing materialized views in data warehousing environment. In: ICDCS, p. 458 (1997)
Zhang, C., Yao, X., Yang, J.: An evolutionary approach to materialized views selection in a data warehouse environment. IEEE Transactions on, Systems, Man, and Cybernetics, Part C: Applications and Reviews 31(3), 282–294 (2001)
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Boukorca, A., Faget, Z., Bellatreche, L. (2014). What-if Physical Design for Multiple Query Plan Generation. In: Decker, H., Lhotská, L., Link, S., Spies, M., Wagner, R.R. (eds) Database and Expert Systems Applications. DEXA 2014. Lecture Notes in Computer Science, vol 8644. Springer, Cham. https://doi.org/10.1007/978-3-319-10073-9_42
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DOI: https://doi.org/10.1007/978-3-319-10073-9_42
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