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Toward an accurate analysis of range queries on spatial data

机译：进行准确的空间数据范围查询分析

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Analysis of range queries on spatial (multidimensional) data is both important and challenging. Most previous analysis attempts have made certain simplifying assumptions about the data sets and/or queries to keep the analysis tractable. As a result, they may not be universally applicable. This paper proposes a set of five analysis techniques to estimate the selectivity and number of index nodes accessed in serving a range query. The underlying philosophy behind these techniques is to maintain an auxiliary data structure, called a density file, whose creation is a one-time cost, which can be quickly consulted when the query is given. The schemes differ in what information is kept in the density file, how it is maintained, and how this information is looked up. It is shown that one of the proposed schemes, called cumulative density (CD), gives very accurate results (usually less than 5 percent error) using a diverse suite of point and rectangular data sets, that are uniform or skewed, and a wide range of query window parameters. The estimation takes a constant amount of time, which is typically lower than 1 percent of the time that it would take to execute the query, regardless of data set or query window parameters.
机译：对空间（多维）数据进行范围查询的分析既重要又具有挑战性。先前的大多数分析尝试都对数据集和/或查询做出了某些简化的假设，以使分析易于处理。因此，它们可能无法普遍适用。本文提出了一套五种分析技术，以估计在为范围查询提供服务时所访问索引节点的选择性和数量。这些技术背后的基本原理是维护一个称为密​​度文件的辅助数据结构，该文件的创建是一次性成本，在发出查询时可以快速查询该结构。方案在密度文件中保留哪些信息，如何维护以及如何查找此信息方面有所不同。结果表明，所提出的一种方案称为累积密度（CD），它使用统一或偏斜且范围广泛的一系列点和矩形数据集，给出了非常准确的结果（通常小于5％的误差）。查询窗口参数。估算花费的时间是恒定的，通常小于执行查询所需时间的1％，而与数据集或查询窗口参数无关。

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