从原始降雨数据中提取风暴中心特征,用于风暴分析和挖掘

Kulsawasd Jitkajornwanich, R. Elmasri, J. McEnery, Chengkai Li
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引用次数: 12

摘要

大多数降雨数据以不容易分析和挖掘的格式存储。在这些格式中,数据量是巨大的。在本文中,我们提出了将原始降雨数据汇总为一个模型的技术,该模型便于风暴分析和挖掘,并减少了数据大小。结果是将原始降雨数据转换为有意义的以风暴为中心的数据,然后将其存储在关系数据库中,以便于分析和挖掘。风暴数据的大小小于原始数据的1%。我们可以确定风暴的时空特征,例如风暴的大小,覆盖的站点数量,风暴的总深度(降水)和持续时间。我们给出了数据转换中需要的与风暴相关的概念的正式定义。然后我们描述了基于这些概念的风暴识别算法。我们的风暴识别算法分析了整个风暴时间段内相邻站点的降水值,并将它们组合在一起以识别整体风暴特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Extracting storm-centric characteristics from raw rainfall data for storm analysis and mining
Most rainfall data is stored in formats that are not easy to analyze and mine. In these formats, the amount of data is enormous. In this paper, we propose techniques to summarize the raw rainfall data into a model that facilitates storm analysis and mining, and reduces the data size. The result is to convert raw rainfall data into meaningful storm-centric data, which is then stored in a relational database for easy analysis and mining. The size of the storm data is less than 1% of the size of the raw data. We can determine the spatio-temporal characteristics of a storm, such as how big a storm is, how many sites are covered, and what is its overall depth (precipitation) and duration. We present formal definitions for the storm-related concepts that are needed in our data conversion. Then we describe storm identification algorithms based on these concepts. Our storm identification algorithms analyze precipitation values of adjacent sites within the period of time that covers the whole storm and combines them together to identify the overall storm characteristics.
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