Abstract
Semantic query optimisation uses knowledge about properties of the data, represented as a set of subset descriptor rules, to transform a query into another form that can be executed in a more efficient manner but still yields the same result as the original query. Commonly this ’semantic knowledge’ in the form of rules is generated either during the query process itself or else is constructed in advance according to defined heuristics. Over a period of time the rule set may, therefore, become very large and the number of semantically equivalent queries that may be derived rises exponentially. Each rule use creates a new equivalent query. The problem is to identify one near optimal alternative query in a time that is minimal and also short relative to the overall query execution time. In this paper we propose a method for measuring the effectiveness of each rule and present a fast algorithm which selects the most cost effective transformations to directly yield the optimal alternative query. Experiments carried out on a large publicly available dataset show worthwhile savings using the approach.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Blasgen, M.W., Eswaran, K.P.: Storage and access in relational data bases. IBM Systems Journal 16(4), 363–377 (1977)
Cardenas, A.F.: Analysis and performance of inverted data base structures. Communications of the ACM 18(5), 253–263 (1975)
Chakravarthy, S., Grant, J., Minker, J.: Logic-based approach to semantic query optimisation. ACM on Database Sys. 15(2), 162–207 (1990)
Chan, K.C., Wong, A.K.C.: A statistical test for extracting classificatory knowledge from databases. In: Knowledge Discovery in Databases, pp. 107–123 (1991)
Graefe, G., Dewitt, D.: The EXODUS optimiser generator. In: Proc. ACM SIGMOD Conference on Management of Data, pp. 160–171 (1987)
Han, J., Cai, Y., Cercone, N.: Data-driven discovery of quantitative rules in relational databases. IEEE Trans. Knowledge and Data Eng. 5(1), 29–40 (1993)
Hsu, C., Knoblock, C.A.: Rule induction for semantic query optimization. In: Proceedings of the 11th International Conference on Machine Learning, pp. 112–120 (1994)
Lowden, B.G.T.: An Approach to Multikey Sequencing in an equiprobable keyterm retrieval situation. In: Proceedings of the 8th ACM SIGIR International Conference on Research and Development in Information Retrieval, pp. 92–96 (1985)
Lowden, B.G.T., Robinson, J., Lim, K.Y.: A semantic query optimiser using automatic rule derivation. In: Proc. WITS 1995, 5th Annual Workshop on Information Technologies and Systems, pp. 68–76 (1995)
Lowden, B.G.T., Robinson, J.: A statistical approach to rule selection in semantic query optimisation. In: Raś, Z.W., Skowron, A. (eds.) ISMIS 1999. LNCS, vol. 1609, pp. 330–339. Springer, Heidelberg (1999)
Mackert, L.F., Lohman, G.M.: R* optimizer validation and performance evaluation for local queries. In: Proc. ACM SIGMOD Conference, pp. 84–95 (1986)
Mannila, H.: Methods and problems in data mining. In: Proc. 6th Intl Conference on Database Theory, pp. 41–55 (1997)
Piatetsky-Shapiro, G., Matheus, C.: Measuring data dependencies in large databases. In: Proc. AAAI Workshop on Knowledge Discovery in Databases, pp. 162–173 (1993)
Robinson, J., Lowden, B.G.T.: Data analysis for query processing. In: Proc. 2nd International Symposium on Intelligent Data Analysis, London, pp. 447–458 (1997)
Robinson, J., Lowden, B.G.T.: Semantic optimisation and rule graphs. In: Proc. 5th KRDB Workshop (1998), http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-10/
Robinson, J., Lowden, B.G.T.: Attribute-pair range rules. In: Quirchmayr, G., Bench-Capon, T.J.M., Schweighofer, E. (eds.) DEXA 1998. LNCS, vol. 1460, pp. 680–691. Springer, Heidelberg (1998)
Shekhar, S., Srivastava, J., Dutta, S.: A formal model of trade-off between optimisation and execution costs in semantic query optimization. In: Proc. 14th VLDB Conference, pp. 457–467 (1988)
Shekhar, S., Hamidzadeh, B., Kohli, A.: Learning transformation rules for semantic query optimisation: A data-driven approach. IEEE Trans. Data & Knowledge Engineering 5(6), 950–964 (1993)
Shenoy, S.T., Ozsoyoglu, Z.M.: Design and implementation of semantic query optimiser. IEEE Transactions on Knowledge and Data Eng. 1(3), 344–361 (1989)
Siegel, M., Sciore, E., Salveter, S.: A method for automatic rule derivation to support semantic query optimisation. ACM Trans. Database Systems 17(4), 563–600 (1992)
Yu, C., Sun, W.: Automatic knowledge acquisition and maintenance for semantic query optimisation. IEEE Trans. Knowledge and Data Engineering 1(3), 362–375 (1989)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lowden, B.G.T., Robinson, J. (2004). Improved Data Retrieval Using Semantic Transformation. In: Galindo, F., Takizawa, M., Traunmüller, R. (eds) Database and Expert Systems Applications. DEXA 2004. Lecture Notes in Computer Science, vol 3180. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30075-5_38
Download citation
DOI: https://doi.org/10.1007/978-3-540-30075-5_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22936-0
Online ISBN: 978-3-540-30075-5
eBook Packages: Springer Book Archive