Data integration: a theoretical perspective
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Logic-based techniques in data integration
Logic-based artificial intelligence
A Uniform Framework for Integrating Knowledge in Heterogeneous Knowledge Systems
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Proceedings of the 17th International Conference on Data Engineering
Quality-driven Integration of Heterogenous Information Systems
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Integration of Biological Data and Quality-Driven Source Negotiation
ER '01 Proceedings of the 20th International Conference on Conceptual Modeling: Conceptual Modeling
Query planning and optimization in information integration
Query planning and optimization in information integration
Optimizing Recursive Information Gathering Plans in EMERAC
Journal of Intelligent Information Systems
Query processing in a geographic mediation system
Proceedings of the 12th annual ACM international workshop on Geographic information systems
ICDEW '06 Proceedings of the 22nd International Conference on Data Engineering Workshops
Fundamentals of Spatial Data Quality (Geographical Information Systems series)
Fundamentals of Spatial Data Quality (Geographical Information Systems series)
Failed-tuple triggered blocking strategy for managing near real-time spatial data replication
Proceedings of the 1st International Conference and Exhibition on Computing for Geospatial Research & Application
Proceedings of the 1st ACM SIGSPATIAL International Workshop on Data Mining for Geoinformatics
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Accurate and efficient integration of geospatial data is an important problem with applications in areas such as emergency response and urban planning. Some of the key challenges in supporting large-scale geospatial data integration are automatically computing the quality of the data provided by a large number of geospatial sources and dynamically providing high quality answers to the user queries based on a quality criteria supplied by the user. We describe a framework called the Quality-driven Geospatial Mediator (QGM) that supports efficient and accurate integration of geospatial data from a large number of sources. The key contributions of our framework are: (1) the ability to automatically estimate the quality of data provided by a source by using the information from another source of known quality, (2) representing the quality of data provided by the sources in a declarative data integration framework, and (3) a query answering technique that exploits the quality information to provide high quality geospatial data in response to user queries. Our experimental evaluation using over 1200 real-world sources shows that QGM can accurately estimate the quality of geospatial sources. Moreover, QGM provides better quality data in response to the user queries compared to the traditional data integration systems and does so with lower response time.