Abstract
Query optimization is an important functionality of modern database systems and often based on estimating the selectivity of queries before actually executing them. Well-known techniques for estimating the result set size of a query are sampling and histogram-based solutions. Sampling-based approaches heavily depend on the size of the drawn sample which causes a trade-off between the quality of the estimation and the time in which the estimation can be executed for large data sets. Histogram-based techniques eliminate this problem but are limited to low-dimensional data sets. They either assume that all attributes are independent which is rarely true for real-world data or else get very inefficient for high-dimensional data. In this paper we present the first multivariate parametric method for estimating the selectivity of window queries for large and high-dimensional data sets. We use clustering to compress the data by generating a precise model of the data using multivariate Gaussian distributions. Additionally, we show efficient techniques to evaluate a window query against the Gaussian distributions we generated. Our experimental evaluation shows that this approach is significantly more efficient for multidimensional data than all previous approaches.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Faloutsos, C., Barber, R., Flickner, M., Hafner, J., Niblack, W., Petkovic, D., Equitz, W.: Efficient and Effective Querying by Image Content. Journal of Intelligent Information Systems 3, 231–262 (1994)
Mehrotra, R., Gary, J.: Feature-Based Retrieval of Similar Shapes. In: Proc. 9th Int. Conf. on Data Engineering, Vienna, Austria, pp. 108–115 (1993)
Shoichet, B.K., Bodian, D.L., Kuntz, I.D.: Molecular Docking Using Shape Descriptors. Journal of Computational Chemistry 13, 380–397 (1992)
Berchtold, S., Keim, D.A., Kriegel, H.P.: The X-Tree: An Index Structure for High-Dimensional Data. In: Proc. 22nd Int. Conf. on Very Large Databases, VLDB 1996 (1996)
Lin, K.I., Jagadish, H.V., Faloutsos, C.: The TV-tree an index structure for high-dimensional data. VLDB Journal: Very Large Data Bases 3, 517–542 (1994)
Weber, R., Schek, H.J., Blott, S.: A Quantitative Analysis and Performance Study for Similarity-Search Methods in High-Dimensional Spaces. In: Proc. 24th Int. Conf. on Very Large Databases (VLDB 1998), pp. 194–205 (1998)
McQueen, J.: Some Methods for Classification and Analysis of Multivariate Observations. In: 5th Berkeley Symp. Math. Statist. Prob., vol. 1, pp. 281–297 (1967)
Sibson, R.: SLINK: An Optimally Efficient Algorithm for the Single-Link Cluster Method. The Computer Journal 16, 30–34 (1973)
Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In: Proc. 2nd Int. Conf. on Knowledge Discovery and Data Mining (KDD 1996), Portland, OR, pp. 291–316 (1996)
Ankerst, M., Breunig, M.M., Kriegel, H.P., Sander, J.: OPTICS: Ordering Points to Identify the Clustering Structure. In: Proc. ACM Int. Conf. on Management of Data, SIGMOD 1999 (1999)
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society, Series B 39, 1–31 (1977)
Selinger, P.G., Astrahan, M.M., Chamberlin, D.D., Lorie, R.A., Price, T.G.: Access Path Selection in a Relational Database Management System. In: Proc. ACM Int. Conf. on Management of Data, SIGMOD 1979 (1979)
Piatetsky-Shapiro, G., Connell, C.: Accurate estimation of the number of tuples satisfying a condition. In: Proc. ACM Int. Conf. on Management of Data, SIGMOD 1984 (1984)
Muralikrishna, M., De Witt, D.J.: Equi-Depth Histograms For Estimating Selectivity Factors For Muli-Dimensional Queries. In: Proc. ACM Int. Conf. on Management of Data, SIGMOD 1988 (1988)
Poosala, V., Ioannidis, Y.E.: Selectivity Estimation without the Attribute Value Independence Assumption. In: Proc. 23rd Int. Conf. on Very Large Databases, VLDB 1997 (1997)
Bruno, N., Chaudhuri, S., Gravan, L.: STHoles: a Multidimensional Workload-aware Histogram. In: Proc. ACM Int. Conf. on Management of Data, SIGMOD 2001 (2001)
Matias, Y., Vitter, J.S., Wang, M.: Wavelet-Based Histograms for Selectivity Estimation. In: Proc. ACM Int. Conf. on Management of Data (SIGMOD 1998), pp. 448–459 (1998)
Lipton, R., Naughton, J.: Query size estimation by adaptive sampling. In: Proc. ACM Symp. on Principles of Database Systems, PODS 1990 (1990)
Lipton, R., Naughton, J., Schneider, D.: Practical selectivity estimation through adaptive sampling. In: Proc. ACM Int. Conf. on Management of Data, SIGMOD 1990 (1990)
Hou, W.C., Ozsoyoglu, G., Dodgu, E.: Error-constrained Count Query: Evaluation in Relational Databases. In: Proc. ACM Int. Conf. on Management of Data, SIGMOD 1991 (1991)
Chen, C.M., Roussopoulos, N.: Adaptive Selectivity Estimation Using Query Feedback. In: Proc. ACM Int. Conf. on Management of Data, SIGMOD 1994 (1994)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Academic Press, London (2001)
Fayyad, U., Reina, C., Bradley, P.: Initialization of Iterative Refinement Clustering Algorithms. In: Proc. 4th Int. Conf. on Knowledge Discovery and Data Mining, KDD 1998 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Böhm, C., Kriegel, HP., Kröger, P., Linhart, P. (2005). Selectivity Estimation of High Dimensional Window Queries via Clustering. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds) Advances in Spatial and Temporal Databases. SSTD 2005. Lecture Notes in Computer Science, vol 3633. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11535331_1
Download citation
DOI: https://doi.org/10.1007/11535331_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28127-6
Online ISBN: 978-3-540-31904-7
eBook Packages: Computer ScienceComputer Science (R0)