IEEE Internet Computing
Semantic Sensor Information Description and Processing
SENSORCOMM '08 Proceedings of the 2008 Second International Conference on Sensor Technologies and Applications
An Experimental Comparison of RDF Data Management Approaches in a SPARQL Benchmark Scenario
ISWC '08 Proceedings of the 7th International Conference on The Semantic Web
An evaluation of triple-store technologies for large data stores
OTM'07 Proceedings of the 2007 OTM Confederated international conference on On the move to meaningful internet systems - Volume Part II
How much semantic data on small devices?
EKAW'10 Proceedings of the 17th international conference on Knowledge engineering and management by the masses
Proceedings of the 2nd ACM SIGSPATIAL International Workshop on GeoStreaming
SGST: an open source semantic geostreaming toolkit
Proceedings of the 2nd ACM SIGSPATIAL International Workshop on GeoStreaming
Ontology paper: The SSN ontology of the W3C semantic sensor network incubator group
Web Semantics: Science, Services and Agents on the World Wide Web
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Recently, many tools have emerged to manage sensor web data using Semantic Web technologies for effective heterogeneous data integration. However, a remaining challenge is how to manage the massive volumes of sensor data in their semantic form, i.e., Resource Description Framework (RDF) triples. Our survey revealed that most semantic tools either do not have geospatial support, or have severe limitations on providing full GeoSPARQL support and good performance for complex queries. In this paper, we present an open source Semantic Spatiotemporal Data Engine (SSTDE), which incorporates both semantic tools and Geographic Information System (GIS) systems under a hybrid architecture. Our main contribution includes 1) introducing the sub-graph index to substitute the single node index, which results in significant performance gain for a spatiotemporal query; 2) developing a query optimization algorithm based on graph matching; 3) proposing a benchmark test for spatiotemporal query over triple stores. The spatiotemporal SPARQL query is intelligently decomposed and executed on different systems, which significantly improves the query performance by more than a hundred times comparing to other solutions.