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).
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© 2005 Springer-Verlag Berlin Heidelberg
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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
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DOI: https://doi.org/10.1007/11430919_62
Publisher Name: Springer, Berlin, Heidelberg
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