Abstract: The group nearest neighbor query uses group points to provide the optimal solution for the nearest group point in the dataset. The novel type of spatial keyword query called Group Nearest Group (GNG) query will be used to optimize the query. Given a data point set D, a query point set Q and an integer k, the Group Nearest Group query finds a subset of points from D, ? (|?| = k), such that the total distance from all points in Q to the nearest point in ? is no greater than any other subset of points in D. Each nearest point obtained matches at least one of the query keywords. For processing this query several algorithms are proposed. The processing of GNG query consists of Exhaustive Hierarchical Combination algorithm and Subset Hierarchical Refinement algorithm. A group nearest neighbor (GNN) query returns the location of a meeting place that minimizes the aggregate distance from a spread out group of users; for example, a group of users can ask for a restaurant that minimizes the total travel distance from them. The duplicates in the dataset can be identified to improve the search query from the given data. The dataset can be analyzed to find out the duplicates in the data set. The applications of group query come from location-based services, e.g., finding a meeting venue for a group of people such that the traveling distances is minimized. In order to prune large portion of query objects reducing the number of node accesses, this extensive experiments on spatial databases is effective in reducing group query response time which exhibits good scalability with the query objects and the number of query keywords.
Keywords: Boolean spatial keyword query, reverse k Boolean spatial keyword query, road network, query processing.