Efficient Framework for Group Nearest Group Query

Abstract

The Efficient Nearest Neighbor group query (ENNGQ) 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, ω (|ω| ≤ k), such that the total distance from all points in Q to the nearest point in ω 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 ENNGQ query consists of a Data Classifier with De-duplicator Block (DCDB ), Most Frequent Search Cache Engine ( MFSCE ) and Exhaustive Subset Refinement algorithm ( ESRA) . A ENNGQ 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 look for a restaurant that serves Chinese dishes 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 using the DCDB.. The data can also be grouped in such a manner that it can be used for efficient retrieval. The MFSCE is a Deterministic Cache engine which helps to store the most frequently accessed keyword and the location with respect to a reference point. Finally the ESRA is a local search heuristic with the current best value is refined by visiting the child nodes of the block using bounded Priority queues. 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.