Abstract
Skyline queries have emerged as an answer to the need of solving queries that involve user preferences. Although the evaluation of the Skyline operator is costly, its efficient incorporation into an execution plan may decrease the execution time of SQL queries. This process is known as Skyline Query Optimization. Several solutions for Skyline Query Optimization have already been presented. These solutions are most often based on Dynamic Programming (DP), which means that all alternative plans are exhaustively enumerated. This approach loses effectiveness as the search space size increases. On the other hand, stochastic search algorithms have shown to be successful in solving optimization problems arising from standard queries. Our previous studies have shown that Evolutionary Algorithms (EAs), implemented in the form of eaSky, may outperform DP approaches for Skyline Query Optimization. Such a comparison between EAs and DP is necessary, because DP is used in most database management systems despite its aforementioned scaling problem. In this chapter, we present experimental results of eaSky and show that eaSky can achieve better performance than DP for large queries. An extended version of eaSky has been developed, and experimental results show further improvements in its performance as compared to the original eaSky.
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References
Date, C.: A Guide to the SQL Standard. Addison-Wesley Longman Publishing Co., Inc., USA (1989)
Ramakrishnan, R., Gehrke, J.: Database Management Systems. McGraw-Hill Higher Education, New York (2000)
Bennett, K., Ferris, M., Ioannidis, Y.: A genetic algorithm for database query optimization. In: Proceedings of the Fourth International Conference on Genetic Algorithms, California, USA, pp. 400–407 (1991)
Godfrey, P., Shipley, R., Gryz, J.: Maximal vector computation in large data sets. In: Proceedings of the VLDB, Trondheim, Norway, pp. 229–240 (2005)
Borzsonyi, S., Kossmann, D., Stocker, K.: The skyline operator. In: IEEE Conf. on Data Engineering, Heidelberg, Germany, pp. 421–430 (2001)
Tan, K., Eng, P., Ooi, B.: Efficient progressive skyline computation. In: VLDB 2001: Proceedings of the 27th International Conference on Very Large Data Bases, pp. 301–310. Morgan Kaufmann Publishers Inc., San Francisco (2001)
Hafenrichter, B., Kießling, W.: Optimization of relational preference queries. In: Proceedings of the 16th Australasian Database Conference, Newcastle, Australia, pp. 175–184 (2005)
Chomicki, J.: Semantic optimization techniques for preference queries. Inf. Syst. 32(5), 670–684 (2007)
Chaudhuri, S., Dalvi, N., Kaushik, R.: Robust cardinality and cost estimation for skyline operator. In: Proceedings of the ICDE, Atlanta, USA, pp. 64–74 (2006)
Eder, H.: On extending postgresql with the skyline operator. Master’s thesis. Vienna University of Technology (2009)
Hong, J., Kao, C., Liu, B.: A genetic algorithm for database query optimization. In: Proceedings of the First IEEE World Congress on Computational Intelligence, Orlando, USA, pp. 350–355 (1994)
Ioannidis, Y.: Query optimization. ACM Computing Surveys 28(1), 121–123 (1996)
Di Bartolo, F., Goncalves, M., Martínez, I., Sardá, F.: An evolutionary algorithm for skyline-join query optimization. In: Proceedings of Portuguese Conference on Artificial Inteligence, Guimaraes, Portugal, pp. 276–287 (2007)
Stonebraker, M., Rowe, L.: The design of postgres. In: SIGMOD 1986: Proceedings of the 1986 ACM SIGMOD International Conference on Management of Data, pp. 340–355. ACM Press, New York (1996)
Astrahan, M., Blasgen, M., Chamberlin, D., Eswaran, K., Gray, J., Griffiths, P., King, W., Lorie, R., McJones, P., Mehl, J., Putzolu, G., Traiger, I., Wade, B., Watson, V.: System r: Relational approach to database management. ACM Trans. Database Syst. 1(2), 97–137 (1976)
Bayir, M., Toroslu, I., Cosar, A.: Genetic algorithm for the multiple-query optimization problem. IEEE Transactions on Systems, Man and Cybernetics, Part C 37, 147–153 (2007)
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer-Verlag GmbH, Germany (1996)
Papadias, D., Tao, Y., Fu, G., Seeger, B.: An optimal and progressive algorithm for skyline queries. In: SIGMOD 2003: Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data, pp. 467–478. ACM Press, New York (2003)
Chomicki, J.: Preference formulas in relational queries. ACM Trans. Database Syst. 28(4), 427–466 (2003)
Brando, C., Goncalves, M., González, V.: Peaqock: A postgresql extension with evaluation algorithms for skyline and top-k skyline queries. In: Proceedings of the CLEI, San José, Costa Rica, pp. 1–11 (2007)
Bentley, J., Kung, H.T., Schkolnick, M., Thompson, C.D.: On the average number of maxima in a set of vectors and applications. Journal of the ACM 25(4), 536–543 (1978)
Godfrey, P.: Cardinality estimation of skyline queries. Technical Report CS-2002-03, York University (2002)
Poli, R., Langdon, W., McPhee, N.: A Field Guide to Genetic Programming. Lulu Press (2008)
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Goncalves, M., Martínez, I., Escuela, G., Di Bartolo, F., Sardá, F. (2012). An Evolutionary Algorithm for Skyline Query Optimization. In: Chiong, R., Weise, T., Michalewicz, Z. (eds) Variants of Evolutionary Algorithms for Real-World Applications. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23424-8_13
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DOI: https://doi.org/10.1007/978-3-642-23424-8_13
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