大规模科学数据中有效的距离分布查询

Abon Chaudhuri, Teng-Yok Lee, Han-Wei Shen, T. Peterka
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引用次数: 2

摘要

如果可能的话,频繁访问原始数据对于回答大规模数据的查询已不再实际。这导致使用基于分布的数据摘要,它可以代替原始数据来回答不同类型的统计查询。我们的工作涉及范围分布查询,它返回任何大小的轴对齐区域的分布。我们解决了在大数据存在的情况下保持这些查询结果的交互性和准确性的挑战。本文提出了一种新颖有效的框架,用于预计算和存储一组分布,这些分布可用于在后处理期间查询任意区域。我们采用一种基于积分图像的数据结构来在恒定时间内回答这类查询,并提出了一种基于相似性的编码技术来降低数据结构的存储成本。我们的方案利用了数据中不同区域之间存在的相似性,因此,它们各自的分布。我们将演示在各种应用程序中使用我们的技术,这些应用程序直接或间接地需要发行版。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Efficient range distribution query in large-scale scientific data
Frequent access to raw data is no longer practical, if possible at all, for answering queries on large-scale data. This has led to the use of distribution-based data summaries, which can substitute for raw data to answer statistical queries of different kinds. Our work is concerned with range distribution query, which returns the distribution of an axis-aligned region of any size. We address the challenge of maintaining the interactivity and accuracy of such query results in the presence of large data. This work presents a novel and efficient framework for pre-computing and storing a set of distributions which can be used to query any arbitrary region during post-processing. We adapt an integral image based data structure to answer such queries in constant time, and propose a similarity-based encoding technique to reduce the storage cost of the data structure. Our scheme utilizes the similarity present among different regions in the data, and hence, their respective distributions. We demonstrate the use our technique in various applications, which directly or indirectly require distributions.
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