Christopher Barton (Committee Member), Kate Beard (Committee Member), Soon Chung (Committee Member), Amit Sheth (Advisor), Krishnaprasad Thirunarayan (Committee Member)
Doctor of Philosophy (PhD)
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.
Department or Program
Department of Computer Science and Engineering
Year Degree Awarded
Copyright 2008, all rights reserved. This open access ETD is published by Wright State University and OhioLINK.