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In this paper, the authors consider the problem of discovering GIS data sources on the web. Source discovery queries for GIS data are specified using keywords and a region of interest. A source is considered relevant if it contains data that matches the keywords in the specified region. Existing techniques simply rely on textual metadata accompanying such datasets to compute relevance to user-queries. Such approaches result in poor search results, often missing the most relevant sources on the web. They address this problem by developing more meaningful summaries of GIS datasets that preserve the spatial distribution of keywords. They conduct experiments showing the effectiveness of proposed summarization techniques by significantly improving the quality of query results over baseline approaches, while guaranteeing scalability and high performance.
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