Abstract
In many fields and applications, it is critical for users to make decisions through OLAP queries. How to promote accuracy and efficiency while answering multiple aggregate queries, e.g. COUNT, SUM, AVG, MAX, MIN and MEDIAN? It has been the urgent problem in the fields of OLAP and data summarization recently. There have been a few solutions such as MRA-Tree and GENHIST for it. However, they could only answer a certain aggregate query which was defined in a particular data cube with some limited applications. In this paper, we develop a novel framework ADenTS, i.e. Adaptive Density-based Tree Structure, to answer various types of aggregate queries within a single data cube. We represent the whole cube by building a coherent tree structure. Several techniques for approximation are also proposed. The experimental results show that our method outperforms others in effectiveness.
This research is supported in part by the Key Program of National Natural Science Foundation of China (No. 69933010 and 60303008), China National 863 High-Tech Projects (No. 2002AA4Z3430 and 2002AA231041).
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Chakrabarti, Garofalakis, M.N., Rastogi, R., Shim, K.: Approxmate Query Processing Using Wavelets. In: Proc. 26th Int. Conf. on Very Large Data Base (VLDB 2000), Cairo, Egypt (September 2000)
Gunopulos, D., Kollios, G., Tsotras, V.J.: Approximating Multi-dimensional Aggregate Range Queries over Real Attributes. In: Proc. ACM SIGMOD 19th Int. Conf. on Management of Data (SIGMOD 2000), Dallas, USA (May 2000)
Lee, J., Kim, D., Chung, C.: Multi-dimensional Selectivity Estimation Using Compressed Histogram Information. In: Proc. ACM SIGMOD 18th Int. Conf. on Management of Data (SIGMOD 1999), Philadelphia, USA (June 1999)
Lazaridis, I., Mehrotra, S.: Progressive Approximate Aggregate Queries with a Multi-Resolution Tree Structure. In: Proc. ACM SIGMOD 20th Int. Conf. on Management of Data (SIGMOD 2001), Santa Barbara, USA (May 2001)
Shanmugasundaram, J., Fayyad, U., Bradley, P.S.: Compressed Data Cubes for OLAP Aggregate Query Approximation on Continuous Dimensions. In: Proc. ACM SIGKDD 6th Int. Conf. on Knowledge Discovery and Data Mining (KDD 1999), San Diego, USA (August 1999)
Vitter, J.S., Wang, M.: Approximate Computation of Multi-dimensional Aggregates of Sparse Data Using Wavelets. In: Proc. ACM SIGMOD 18th Int. Conf. on Management of Data (SIGMOD 1999), Philadelphia, USA (June 1999)
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
Wu, T., Xu, J., Wang, C., Wang, W., Shi, B. (2005). ADenTS: An Adaptive Density-Based Tree Structure for Approximating Aggregate Queries over Real Attributes. In: Ho, T.B., Cheung, D., Liu, H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2005. Lecture Notes in Computer Science(), vol 3518. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11430919_62
Download citation
DOI: https://doi.org/10.1007/11430919_62
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26076-9
Online ISBN: 978-3-540-31935-1
eBook Packages: Computer ScienceComputer Science (R0)