quality-driven approximate methods for integrating gis data
GIS data distributed in local, state, federal, and private data clearinghouses are being made accessible through the efforts of organizations such as Federal Geographic Data Committee (FGDC) and GeoData.gov. Many database applications, such as disaster management, transportation, and national infrastructure protection, need to access GIS information from such various data sources. In this paper we study how to answer keyword-based spatial queries approximately using information from heterogeneous GIS sources. An example query specifies the region of Orange County and keywords "junior schools," which asks for geospatial objects relevant to junior schools in Orange County. The answers to such a query provided by different sources differ widely in their content and quality. It is computationally expensive to access all the datasets to retrieve all the relevant objects. We develop approximate algorithms for answering such queries based on the local analysis of the query region using space-partitioning techniques. Our methods rank datasets in a partition based on parameters such as their spatial coverage and content matching the query keywords. The quality of the answers keeps improving progressively as we do deeper local analysis. We develop an efficient traversal strategy to maximize the quality refinement within a given time limit. We conducted experiments to evaluate the proposed techniques.
approximate methods gis data integration heterogeneous data sources