The K Group Nearest-Neighbor Query on Non-indexed RAM-Resident Data

  • George Roumelis
  • Michael Vassilakopoulos
  • Antonio CorralEmail author
  • Yannis Manolopoulos
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 582)


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.


Spatial query processing Plane-sweep Group nearest-neighbor query Algorithms 


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • George Roumelis
    • 1
  • Michael Vassilakopoulos
    • 2
  • Antonio Corral
    • 3
    Email author
  • Yannis Manolopoulos
    • 1
  1. 1.Department of InformaticsAristotle University of ThessalonikiThessalonikiGreece
  2. 2.Department of Electrical and Computer EngineeringUniversity of ThessalyVolosGreece
  3. 3.Department of InformaticsUniversity of AlmeriaAlmeríaSpain

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