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. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
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 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3518 LNAI, pp. 529–538). Springer Verlag. https://doi.org/10.1007/11430919_62
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