Conceptual Modeling: Foundations and Applications
Deep integration of spatial query processing into native RDF triple stores
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
An ontology based personal exposure history
Proceedings of the 1st ACM International Health Informatics Symposium
SGST: an open source semantic geostreaming toolkit
Proceedings of the 2nd ACM SIGSPATIAL International Workshop on GeoStreaming
ESWC'10 Proceedings of the 7th international conference on The Semantic Web: research and Applications - Volume Part I
Strabon: a semantic geospatial DBMS
ISWC'12 Proceedings of the 11th international conference on The Semantic Web - Volume Part I
Enabling the geospatial Semantic Web with Parliament and GeoSPARQL
Semantic Web - On linked spatiotemporal data and geo-ontologies
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Spatial and temporal data are critical components in many applications. This is especially true in analytical applications ranging from scientific discovery to national security and criminal investigation. The analytical process often requires uncovering and analyzing complex thematic relationships between disparate people, places and events. Fundamentally new query operators based on the graph structure of Semantic Web data models, such as semantic associations, are proving useful for this purpose. However, these analysis mechanisms are primarily intended for thematic relationships. This dissertation proposes a framework built around the RDF data model for analysis of thematic, spatial and temporal relationships between named entities. We present a spatiotemporal modeling approach that uses an upper-level ontology in combination with temporal RDF graphs. A set of query operators that use graph patterns to specify a form of context are formally defined, and an extension of the W3C-recommended SPARQL query language to support these query operators is presented. We also describe an efficient implementation of the framework that extends a state-of-the-art commercial database system. We demonstrate the scalability of our approach with a performance study using both synthetic and real-world RDF datasets of over 25 million triples.