Monochromatic and bichromatic reverse top-k group nearest neighbor queries

摘要: We formalize and solve the monochromatic bichromatic RkGNN problem.We propose an algorithm to efficiently compute queries.Some effective pruning methods are proposed reduce overhead of processing.Our is several orders magnitude faster than linear scan algorithm. The Group Nearest Neighbor (GNN) search important approach for expert intelligent systems, i.e., Geographic Information System (GIS) Decision Support (DSS). However, traditional GNN starts from users' perspective selects locations or objects that users like. Such applications fail help managers since they do not provide managerial insights. In this paper, we focus on solving problem managers' perspective. particular, a novel query, namely, reverse top-k group nearest neighbor (RkGNN) query which returns k groups data so each has object q as their (GNN). This tool decision support, e.g., location-based service, product analysis, trip planning, disaster management because it provides analysts intuitive way finding significant with respect q. Despite importance, kind queries received adequate attention research community challenging task answer queries. To end, first in both cases, then methods, sorting threshold pruning, MBR property window space during processing. Furthermore, improve performance by employing reuse heap technique. As extension also study interesting variant namely constrained (CRkGN) query. Extensive experiments using synthetic real datasets demonstrate efficiency effectiveness our approaches.