大空间数据时代的时空数据挖掘:算法与应用

Ranga Raju Vatsavai, A. Ganguly, V. Chandola, A. Stefanidis, S. Klasky, S. Shekhar
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引用次数: 147

摘要

空间数据挖掘是从空间和时空数据中发现有趣的、以前未知的、但可能有用的模式的过程。然而,空间和时空数据的爆炸式增长,以及社交媒体和位置传感技术的出现,强调了开发新的、计算效率高的方法来分析大数据的必要性。在本文中,我们通过密切关注计算和I/O需求来回顾主要的空间数据挖掘算法,并暗示一些处理大空间数据的应用程序。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Spatiotemporal data mining in the era of big spatial data: algorithms and applications
Spatial data mining is the process of discovering interesting and previously unknown, but potentially useful patterns from the spatial and spatiotemporal data. However, explosive growth in the spatial and spatiotemporal data, and the emergence of social media and location sensing technologies emphasize the need for developing new and computationally efficient methods tailored for analyzing big data. In this paper, we review major spatial data mining algorithms by closely looking at the computational and I/O requirements and allude to few applications dealing with big spatial data.
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