In the current era of big spatial data, the vast amount of produced mobility data (by sensors, GPS-equipped devices, surveillance networks, radars, etc.) poses new challenges related to mobility analytics. A cornerstone facilitator for performing mobility analytics at scale is the availability of big data processing frameworks and techniques tailored for spatial and spatio-temporal data. Motivated by this pressing need, in this paper, we provide a survey of big data processing frameworks for mobility analytics. Particular focus is put on the underlying techniques; indexing, partitioning, query processing are essential for enabling efficient and scalable data management. In this way, this report serves as a useful guide of state-of-the-art methods and modern techniques for scalable mobility data management and analytics.

中文翻译：

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移动分析大数据处理框架调查

在当前大空间数据时代，产生的大量移动数据（通过传感器、配备 GPS 的设备、监控网络、雷达等）对移动分析提出了新的挑战。大规模执行移动分析的一个基石促进因素是大数据处理框架和为空间和时空数据量身定制的技术的可用性。受这种紧迫需求的推动，在本文中，我们对用于移动分析的大数据处理框架进行了调查。特别关注底层技术；索引、分区、查询处理对于实现高效和可扩展的数据管理至关重要。通过这种方式，本报告可作为可扩展移动数据管理和分析的最新方法和现代技术的有用指南。