Title :
Spatiotemporal query processing for semantic data stream
Author :
Sungkwang Eom ; Sangjin Shin ; Kyong-Ho Lee
Author_Institution :
Dept. of Comput. Sci., Yonsei Univ., Seoul, South Korea
Abstract :
In this paper, we propose a method for processing spatiotemporal queries on semantic data streams generated from diverse sensors. On the Internet of Things (IoT) environment, the number of mobile sensors greatly increases and their locations are becoming more important. IoT services may not be fully supported when only considering the temporal feature of streaming data. Accordingly, stream processing should be performed with consideration into both temporal and spatial factors. However, existing researches have a limitation of processing spatial queries since they focus on the temporal processing of streaming data. To support spatiotemporal query processing on semantic data streams, we propose a query language, which integrates temporal and geospatial properties. Specifically, we construct a spatiotemporal index to process the proposed spatiotemporal query language efficiently. The experimental results with a prototype implementation show that the proposed method processes spatiotemporal queries in an acceptable time.
Keywords :
Internet of Things; data handling; mobile computing; query languages; query processing; sensor fusion; Internet of Things; IoT environment; IoT services; geospatial properties; mobile sensors; semantic data stream; spatial factor; spatial query processing; spatiotemporal index; spatiotemporal query language; spatiotemporal query processing; stream processing; streaming data temporal feature; temporal factor; temporal processing; temporal properties; Complexity theory; Indexes; internet of things; semantic data; spatiotemporal query language; stream processing;
Conference_Titel :
Semantic Computing (ICSC), 2015 IEEE International Conference on
Conference_Location :
Anaheim, CA
DOI :
10.1109/ICOSC.2015.7050822