Abstract
The purpose of selectivity estimation is to minimize the error of estimated value and query result using the summary data maintained on small memory space. Many works have been performed to estimate accurately selectivity. However, the existing works require a large amount of memory to retain accurate selectivity. In order to solve this problem, we propose a new technique cumulative density wavelet histogram, called CDW Histogram which is able to compress summary data and get an accurate selectivity in small memory space. The proposed method is based on the sub-histograms created by CD histogram and the wavelet transformation technique. The experimental results showed that the proposed method is superior to the existing selectivity estimation technique.
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.
Similar content being viewed by others
References
Ioannidis, Y.E., Poosala, V.: Histogram-Based Solutions to Diverse Database Estimation Problems. IEEE Data Engineering Bulletin 18(3), 10–18 (1995)
Poosala, V., Haas, P.J., Ioannidis, Y.E.: Improved Histograms for Selectivity Estimation of Range Predicates. In: ACM SIGMOD, pp. 294–305. ACM Press, New York (1996)
Stollnitz, E., DeRose, T., Salesin, D.: Wavelet for Computer Graphics Theory and Applications. Morgan Kaufmann, San Francisco (1996)
Ioannidis, Y.E.: Query Optimization. ACM Computing Surveys 28(1), 121–123 (1996)
Vitter, J.S., Wang, M.: Approximate Computation of Multidimensional Aggregates of Sparse Data using Wavelets. In: ACM SIGMOD, pp. 193–204. ACM Press, New York (1999)
Acharya, S., Poosala, V., Ramaswamy, S.: Selectivity Estimation in Spatial Databases. In: ACM SIGMOD, pp. 13–24. ACM Press, New York (1999)
Jin, J., An, N., Sivasubramaniam, A.: Analyzing Range Queries on Spatial Data, In: ICDE, pp. 525–534 (2000)
Matias, Y., Vitter, J.S., Wang, M.: Dynamic Maintenance of Wavelet-Based Histograms, The VLDB Journal, 101–110 (2000)
Wang, M., Vitter, J.S., Lim, L., Padmanabhan, S.: Wavelet-based Cost Estimation for Spatial Queries. In: SSTD, pp. 175–196 (2001)
Choi, Y.J., Chung, C.W.: Selectivity Estimation for Spatio-Temporal Queries to Moving Objects. In: ACM SIGMOD, pp. 440–451. ACM Press, New York (2002)
Sun, C., Agrawal, D., Abbadi, A., El.:Selectivity for spatial joins with geometric selections. In: EDBT, pp. 609–626 (2002)
Hadjieleftheriou, M., Kollios, G., Tsotras, V.: Performance Evaluation of Spatio-Temporal Selectivity Estimation Techniques. In: SSDB, pp. 202–211 (2003)
Tao, Y., Sun, J., Papadias, D.: Selectivity Estimation for Predictive Spatio-Temporal Queries. In: ICDE, 417–428 (2003)
Zhang, Q., Lin, X.: Clustering Moving Objects for Spatio-Temporal Selectivity Estimation. In: ADC, pp. 123–130 (2004)
Chi, J.H., Kim, S.H., Ryu, K.H.: Spatial Selectivity Estimation using Compressed Histogram Information. In: APWeb, pp. 489–494 (2005)
Elmongui, H.G., Mokbel, M.F., Aref, W.G.: Spatio-temporal Histogram. In: SSTD, pp. 19–36 (2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cho, B.K. (2007). Spatial Selectivity Estimation Using Cumulative Density Wavelet Histogram. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2007. Lecture Notes in Computer Science(), vol 4682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74205-0_54
Download citation
DOI: https://doi.org/10.1007/978-3-540-74205-0_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74201-2
Online ISBN: 978-3-540-74205-0
eBook Packages: Computer ScienceComputer Science (R0)