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用于时空分析的数据驱动方法:研究现状综述

Data-Driven Approaches for Spatio-Temporal Analysis: A Survey of the State-of-the-Arts

  • 摘要: 随着目前的电信、传感器网络、众包和远程遥感技术的进步,时空数据的体量飞速增长。随着机器学习和智能计算技术的发展,大量可用的时空(ST)数据激发了研究者研究开发不同的数据驱动模型,以从这些大时空数据集中抽取有用和有趣的模式、关系和知识。本文结构化并系统化地概述了用于时空数据分析的数据驱动方法,重点是当前各种时空数据挖掘技术及其在不同领域的应用。首先,我们简要介绍时空数据的特点,以及分析此类数据的诸多挑战,并总结了此跨学科领域里关于此研究的当前趋势和远景。与其它相关综述相比,本文从计算/方法论和应用两个角度,更加全面地描述了相关技术。我们期望此综述能为理解用于时空数据分析的数据驱动的建模方面的不同研究方向提供帮助。

     

    Abstract: With the advancement of telecommunications, sensor networks, crowd sourcing, and remote sensing technology in present days, there has been a tremendous growth in the volume of data having both spatial and temporal references. This huge volume of available spatio-temporal (ST) data along with the recent development of machine learning and computational intelligence techniques has incited the current research concerns in developing various data-driven models for extracting useful and interesting patterns, relationships, and knowledge embedded in such large ST datasets. In this survey, we provide a structured and systematic overview of the research on data-driven approaches for spatio-temporal data analysis. The focus is on outlining various state-of-the-art spatio-temporal data mining techniques, and their applications in various domains. We start with a brief overview of spatio-temporal data and various challenges in analyzing such data, and conclude by listing the current trends and future scopes of research in this multi-disciplinary area. Compared with other relevant surveys, this paper provides a comprehensive coverage of the techniques from both computational/methodological and application perspectives. We anticipate that the present survey will help in better understanding various directions in which research has been conducted to explore data-driven modeling for analyzing spatio-temporal data.

     

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