Abstract
We have developed an adaptive selectivity estimation scheme for multidimensional queries which, experiments indicate, performs better than previously formulated non-adaptive methods when the distribution of the data is not known. Our approach uses a technique based on dynamic quantized spaces, a dynamic data structure developed for motion analysis in the field of computer vision. The objective of this research is to overcome the disadvantages of previously formulated non-adaptive, static methods which are relatively inaccurate in a dynamic database environment when the distribution of the data is not uniform. We have shown via many experiments that our approach is more flexible and more accurate in the computation of selectivity factors than both the equi-width and equi-depth histogram methods when the database is large and undergoes frequent update activity following a non-uniform distribution.
This is a preview of subscription content, log in via an institution.
Preview
Unable to display preview. Download preview PDF.
References
J.L. Bentley: Multidimensional Binary Search Trees in Database Applications. IEEE Transactions on Software Engineering SE-5, 333–340 (1979)
M.C. Chen, L. McNamee, and N. Matloff: Selectivity Estimation Using Homogeneity Measurement. Proceedings of the 6th International Conference on Data Engineering, Los Angeles, California, 304–310 (February 1990)
R.J. Lipton, J.F. Naughton, and D.A. Schneider: Practical Selectivity Estimation through Adaptive Sampling. Proceedings of the ACM-SIGMOD International Conference on Management of Data, Atlantic City, New Jersey, 1–11 (June 1990)
D.B. Lomet and B. Salzberg: A Robust Multi-Attribute Search Structure. Proceedings of the 5th International Conference on Data Engineering, Los Angeles, California, 296–304 (February 1989)
M. Muralikrishna and D.J. DeWitt: Equi-Depth Histograms for Estimating Selectivity Factors for Multi-Dimensional Queries. Proceedings of the ACM-SIGMOD International Conference on Management of Data, Chicago, Illinois, 28–36 (June 1988)
J. Nievergelt, H. Hinterberger, and K.C. Sevcik: The Grid File: An Adaptable Symmetric Multikey File Structure. ACM Transactions on Database Systems, 9, 38–71 (1984)
J. O'Rourke and K.R. Sloan, Jr.: Dynamic Quantization: Two Adaptive Data Structures for Multidimensional Spaces. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-6, 266–280 (1984)
S. Salza and M. Terranova: Evaluating the Size of Queries on Relational Databases with non-Uniform Distribution and Stochastic Dependence. Proceedings of the ACM-SIGMOD International Conference on the Management of Data, Portland, Oregon, 8–14 (June 1989)
S.T. Shenoy and Z.M. Ozsoyoglu: A System for Semantic Query Optimization. Proceedings of the ACM-SIGMOD International Conference on Management of Data, San Francisco, California, 181–195 (May 1987)
T. Sellis, N. Roussopoulos, and C. Faloutsos: R+-Tree-A Dynamic Index for Multidimensional Objects. Proceedings of the 13th International Conference on Very Large Databases, Brighton, England, 507–518 (September 1987)
C.T. Yu and W. Sun: Automatic Knowledge Acquisition and Maintenance for Semantic Query Optimization. IEEE Transactions on Knowledge and Data Engineering, 1, 362–375 (1989)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1993 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Grosky, W.I., Sun, J., Fotouhi, F. (1993). Dynamic selectivity estimation for multidimensional queries. In: Lomet, D.B. (eds) Foundations of Data Organization and Algorithms. FODO 1993. Lecture Notes in Computer Science, vol 730. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-57301-1_14
Download citation
DOI: https://doi.org/10.1007/3-540-57301-1_14
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-57301-2
Online ISBN: 978-3-540-48047-1
eBook Packages: Springer Book Archive