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.
All Science Journal Classification (ASJC) codes
- Information Systems
- Media Technology
- Computer Science Applications
- Management Science and Operations Research
- Library and Information Sciences