Spatiotemporal data mining aims to discover interesting, useful but non-trivial patterns in big spatial and spatiotemporal data. They are used in various application domains such as public safety, ecology, epidemiology, earth science, etc. This problem is challenging because of the high societal cost of spurious patterns and exorbitant computational cost. Recent surveys of spatiotemporal data mining need update due to rapid growth. In addition, they did not adequately survey parallel techniques for spatiotemporal data mining. This paper provides a more up-to-date survey of spatiotemporal data mining methods. Furthermore, it has a detailed survey of parallel formulations of spatiotemporal data mining.
翻译:外现数据挖掘旨在发现大型空间和时空数据中有趣、有用但非三角模式,用于公共安全、生态、流行病学、地球科学等各种应用领域。由于虚假模式和高昂计算成本的社会成本很高,这一问题具有挑战性。最近对中时数据挖掘的调查由于快速增长而需要更新。此外,它们没有适当调查空间和时空数据挖掘的平行技术。本文提供了对空间数据挖掘方法的最新调查。此外,它还详细调查了空间和时空数据挖掘的平行配制。