{"id":158734,"date":"2010-04-01T00:00:00","date_gmt":"2010-04-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/answering-top-k-similar-region-queries\/"},"modified":"2018-10-16T20:45:07","modified_gmt":"2018-10-17T03:45:07","slug":"answering-top-k-similar-region-queries","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/answering-top-k-similar-region-queries\/","title":{"rendered":"Answering Top-k Similar Region Queries"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Advances in web technology have given rise to new information retrieval applications. In this paper, we present a model for geographical region search and call this class of query similar region query. Given a spatial map and a query region, a similar region search aims to find the top-k most similar regions to the query region on the spatial map. We design a quadtree based algorithm to access the spatial map at different resolution levels. The proposed search technique utilizes a filter-and-refine manner to prune regions that are not likely to be part of the top-k results, and refine the remaining regions. Experimental study based on a real world dataset verifies the effectiveness of the proposed region similarity measure and the efficiency of the algorithm.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Advances in web technology have given rise to new information retrieval applications. In this paper, we present a model for geographical region search and call this class of query similar region query. 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