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Upper Borders for Emerging Cubes

机译:新兴多维数据集的上限

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The emerging cube computed from two relations r_1, r_2 of categorical attributes gather the tuples for which the measure value strongly increases from r_1 to r_2. In this paper, we are interested in borders for emerging cubes which optimize both storage space and computation time. Such borders also provide classification and cube navigation tools. Firstly we study the condensed representation through the classical borders Lower / Upper, then we propose the borders Upper~* / Upper more reduced than the previous ones. We soundly state the connexion between the two representations by using cube transversals. Finally, we perform experiments about the size of the introduced representations. The results are convincing and reinforce our idea that the proposed borders are relevant candidates to be the smallest condensed representation of emerging cubes and thus can be really interesting for trend analysis in Olap databases.
机译:从类别属性的两个关系r_1,r_2计算出的新兴多维数据集会收集元组,对于这些元组,度量值会从r_1急剧增加到r_2。在本文中,我们对可优化存储空间和计算时间的新兴多维数据集的边界感兴趣。这样的边界还提供了分类和多维数据集导航工具。首先,我们通过经典边界Lower / Upper来研究压缩表示,然后我们提出了比以前的边界更简化的Upper _ * / Upper边界。我们通过使用立方体横断面来良好地陈述两个表示之间的连接。最后,我们对引入的表示的大小进行实验。结果令人信服并强化了我们的想法,即建议的边界是新兴多维数据集的最小压缩表示形式的相关候选者,因此对于Olap数据库中的趋势分析可能非常有趣。

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