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Efficient generation of spatiotemporal relationships from spatial data streams and static data

机译：从空间数据流和静态数据有效生成时空关系

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摘要

Recently, a massive amount of position-annotated data is being generated in a stream fashion. Also, massive amounts of static data including spatial features are collected and made available. In the Internet of Things (IoT) environments, various applications can get benefits by utilizing spatial data streams and static data. Therefore, IoT applications typically require stream processing and reasoning capabilities that extract information from low-level data. Particularly for sophisticated stream processing and reasoning, spatiotemporal relationship (SR) generation from spatial data streams and static data must be preceded. However, existing techniques mostly focus solely on direct processing of sensing data or generation of spatial relationships from static data. In this paper, we first address the importance of SRs between spatial data streams and static data and then propose an efficient approach of deriving SRs in real-time. We design a novel R-tree-based index with Representative Rectangles (RRs) called R3 index and devise an algorithm that leverages relationships and distances between RRs to generate SRs. To verify the effectiveness and efficiency of the proposed approach, we performed experiments using real-world datasets. Through the results of the experiments, we confirmed the superiority of the proposed approach.
机译：近来，以流方式生成了大量的带有位置注释的数据。同样，包括空间特征在内的大量静态数据也被收集并可用。在物联网（IoT）环境中，各种应用程序都可以利用空间数据流和静态数据来受益。因此，IoT应用程序通常需要从低级数据中提取信息的流处理和推理功能。特别是对于复杂的流处理和推理，必须先从空间数据流和静态数据生成时空关系（SR）。但是，现有技术大多仅专注于直接处理感测数据或从静态数据生成空间关系。在本文中，我们首先解决了空间数据流和静态数据之间SR的重要性，然后提出了一种实时导出SR的有效方法。我们设计了一种具有代表性矩形（RR）的新颖的基于R树的索引，称为R3索引，并设计了一种算法，该算法利用RR之间的关系和距离来生成SR。为了验证所提方法的有效性和效率，我们使用了真实的数据集进行了实验。通过实验结果，我们证实了所提出方法的优越性。

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