The K Group Nearest-Neighbor Query on Non-indexed RAM-Resident Data
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Data sets that are used for answering a single query only once (or just a few times) before they are replaced by new data sets appear frequently in practical applications. The cost of buiding indexes to accelerate query processing would not be repaid for such data sets. We consider an extension of the popular (K) Nearest-Neighbor Query, called the (K) Group Nearest Neighbor Query (GNNQ). This query discovers the (K) nearest neighbor(s) to a group of query points (considering the sum of distances to all the members of the query group) and has been studied during recent years, considering data sets indexed by efficient spatial data structures. We study (K) GNNQs, considering non-indexed RAM-resident data sets and present an existing algorithm adapted to such data sets and two Plane-Sweep algorithms, that apply optimizations emerging from the geometric properties of the problem. By extensive experimentation, using real and synthetic data sets, we highlight the most efficient algorithm.