Aggregate Nearest Keyword Search in Spatial Databases

Given a set of spatial points $D$ containing keywords information, a set of query objects Q and m query keywords, a top-k aggregate nearest keyword (ANK) query retrieves k objects from Q with the minimum sum of distances to its nearest points in D such that each nearest point matches at least one of query keywords. For example, consider there is a spatial database D which manages facilities (e.g., school, restaurants, hospital, etc.) represented by sets of keywords. A user may want to rank a set of locations with respect to the sum of distances to nearest interested facilities. For processing this query, several algorithms are proposed using IR2-Tree as index structure. Experiments on real data sets indicate that our approach is scalable and efficient in reducing query response time.