TY - GEN
T1 - Spatiotemporal query processing for semantic data stream
AU - Eom, Sungkwang
AU - Shin, Sangjin
AU - Lee, Kyong Ho
PY - 2015/2/26
Y1 - 2015/2/26
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84925651446&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84925651446&partnerID=8YFLogxK
U2 - 10.1109/ICOSC.2015.7050822
DO - 10.1109/ICOSC.2015.7050822
M3 - Conference contribution
AN - SCOPUS:84925651446
T3 - Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing, IEEE ICSC 2015
SP - 290
EP - 297
BT - Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing, IEEE ICSC 2015
A2 - Kankanhalli, Mohan S.
A2 - Li, Tao
A2 - Wang, Wei
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th IEEE International Conference on Semantic Computing, IEEE ICSC 2015
Y2 - 7 February 2015 through 9 February 2015
ER -