Abstract

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 …

Date

November 7, 2007

Authors

Snehal Thakkar, Craig A Knoblock, Jose Luis Ambite

Book

Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems

Pages

1-8