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A quad-tree based multiresolution approach for two-dimensional summary data

机译：二维汇总数据的基于四叉树的多分辨率方法

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摘要

In many application contexts, like statistical databases, scientific databases, query optimizers, OLAP, and so on, data are often summarized into synopses of aggregate values. Summarization has the great advantage of saving space, but querying aggregate data rather than the original ones introduces estimation errors which cannot be in general avoided, as summarization is a lossy compression. A central problem in designing summarization techniques is to retain a certain degree of accuracy in reconstructing query answers. In this paper we restrict our attention to two-dimensional data, which are relevant for a number of applications, and propose a hierarchical summarization technique, which is combined with the use of indices, i.e. compact structures providing an approximate description of portions of the original data. Experimental results show that the technique gives approximation errors much smaller than other "general purpose" techniques, such as wavelets and various types of multi-dimensional histogram.
机译：在许多应用程序上下文中，例如统计数据库，科学数据库，查询优化器，OLAP等，数据通常被汇总为汇总值的摘要。汇总具有节省空间的巨大优势，但是查询汇总数据而不是原始数据会引入估计误差，由于汇总是有损压缩，因此通常无法避免。设计摘要技术时的中心问题是在重构查询答案时保持一定程度的准确性。在本文中，我们将注意力集中在与许多应用程序相关的二维数据上，并提出一种分层汇总技术，该技术与索引的使用相结合，即紧凑的结构提供了原始部分的近似描述。数据。实验结果表明，该技术提供的逼近误差比其他“通用”技术（例如小波和各种类型的多维直方图）小得多。

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