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	1. Efficient Computation of Hierarchical PrefixCube
	Yan Wenyue,Fang Qiong,Wang Yuanzhen
	Computer Science and Technology 28 July 2005
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (0 B)

	Abstract:PrefixCube was proposed to be an efficient cube structure by augmenting BU-BST Condensing with intra-cuboid prefix-sharing. PrefixCube not only has got efficient cube compression ratio but also has made a good compromise among cube compression, restoring and updating costs, and query characteristics. However, it does not directly support dimension hierarchies, on which rollup and drilldown queries quite naturally arise in OLAP. In this paper we extend the PrefixCube architecture for incorporating hierarchical data cubes, i.e. cubes with hierarchical dimensions, and hence get HierPrefixCube. We show that HierPrefixCube retains the advantages on computation and organization of PrefixCube while being able to directly and sufficiently support aggregate queries on levels of dimension hierarchy.
	TO cite this article:Yan Wenyue,Fang Qiong,Wang Yuanzhen. Efficient Computation of Hierarchical PrefixCube[OL].[28 July 2005] http://en.paper.edu.cn/en_releasepaper/content/2549


	2. An Optimization Model for Outlier Detection in Categorical Data
	Zengyou He
	Computer Science and Technology 31 March 2005
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	Abstract:The task of outlier detection is to find small groups of data objects that are exceptional when compared with rest large amount of data. Detection of such outliers is important for many applications such as fraud detection and customer migration. Most existing methods are designed for numeric data. They will encounter problems with real-life applications that contain categorical data. In this paper, we formally define the problem of outlier detection in categorical data as an optimization problem from a global viewpoint. Moreover, we present a local-search heuristic based algorithm for efficiently finding feasible solutions. Experimental results on real datasets and large synthetic datasets demonstrate the superiority of our model and algorithm.
	TO cite this article:Zengyou He. An Optimization Model for Outlier Detection in Categorical Data[OL].[31 March 2005] http://en.paper.edu.cn/en_releasepaper/content/1768

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