Abstract

The growing diffusion of the latest information and communication technologies in different contexts allowed the constitution of enormous sensing networks that form the underlying texture of smart environments. The amount and the speed at which these environments produce and consume data are starting to challenge current spatial data management technologies. In this work, we report on our experience handling real-world spatiotemporal datasets: a stationary dataset referring to the parking monitoring system and a non-stationary dataset referring to a train-mounted railway monitoring system. In particular, we present the results of an empirical comparison of the retrieval performances achieved by three different off-the-shelf settings to manage spatiotemporal data, namely the well-established combination of PostgreSQL + PostGIS with standard indexing, a clustered version of the same setup, and then a combination of the basic setup with Timescale, a storage extension specialized in handling temporal data. Since the non-stationary dataset has put much pressure on the configurations above, we furtherly investigated the advantages achievable by combining the TSMS setup with state-of-the-art indexing techniques. Results showed that the standard indexing is by far outperformed by the other solutions, which have different trade-offs. This experience may help researchers and practitioners facing similar problems managing these types of data.

中文翻译：

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如何在商业 DBMS 中管理来自固定和非固定传感器的海量时空数据集？

摘要

最新的信息和通信技术在不同环境中的不断传播使得能够构建巨大的传感网络，从而构成智能环境的基础结构。这些环境产生和消耗数据的数量和速度开始挑战当前的空间数据管理技术。在这项工作中，我们报告了处理现实世界时空数据集的经验：涉及停车监控系统的静态数据集和涉及列车安装的铁路监控系统的非静态数据集。特别是，我们展示了通过三种不同的现成设置来管理时空数据所实现的检索性能的实证比较结果，即PostgreSQL + PostGIS与标准索引的完善组合，这是相同索引的集群版本设置，然后将基本设置与Timescale相结合，Timescale 是专门用于处理时态数据的存储扩展。由于非平稳数据集给上述配置带来了很大的压力，我们进一步研究了通过将 TSMS 设置与最先进的索引技术相结合可实现的优势。结果表明，标准索引的性能远远优于其他具有不同权衡的解决方案。这种经验可能会帮助研究人员和从业者在管理这些类型的数据时面临类似的问题。