Abstract
In multidatabase systems the heterogeneity and the autonomy of the sites preclude the applicability of the classical query optimization algorithms used in distributed database systems, and therefore new approaches have to be investigated. In this paper we propose a distributed optimization algorithm that makes very general assumptions on the cost function, and on the degree of autonomy of the federated sites. The algorithm is based on a cooperative approach where cost of a query execution plan is evaluated as the composition of the cost estimates produced by the local sites where each part of the computation has to be performed. During the optimization process larger and larger partial execution plans are considered, and the the relative cost estimates are exchanged between the sites. Duplicated and unnecessary computation is avoided by discarding as soon as possible partial plans that are dominated by an equivalent one. The paper substantially extends the results of a previous paper, where we first introduced the idea of cooperative optimization, by removing major restrictions on the query model, and by considering not only sequential execution plans but also parallel ones.
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© 1998 Springer-Verlag Berlin Heidelberg
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Salza, S., Barone, G., Morzy, T. (1998). A distributed algorithm for global query optimization in multidatabase systems. In: Litwin, W., Morzy, T., Vossen, G. (eds) Advances in Databases and Information Systems. ADBIS 1998. Lecture Notes in Computer Science, vol 1475. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0057720
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DOI: https://doi.org/10.1007/BFb0057720
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