(A)kNN query processing on the cloud: A survey

Publikation: Bidrag til bog/antologi/rapportBidrag til bog/antologiForskningfagfællebedømt

  • Nikolaos Nodarakis
  • Angeliki Rapti
  • Spyros Sioutas
  • Athanasios K. Tsakalidis
  • Dimitrios Tsolis
  • Giannis Tzimas
  • Panagis, Ioannis
© Springer International Publishing AG 2017. A k-nearest neighbor (kNN) query determines the k nearest points, using distance metrics, from a given location. An all k-nearest neighbor (AkNN) query constitutes a variation of a kNN query and retrieves the k nearest points for each point inside a database. Their main usage resonates in spatial databases and they consist the backbone of many location-based applications and not only. Although (A)kNN is a fundamental query type, it is computationally very expensive. During the last years a multiplicity of research papers has focused around the distributed (A)kNN query processing on the cloud. This work constitutes a survey of research efforts towards this direction. The main contribution of this work is an up-to-date review of the latest (A)kNN query processing approaches. Finally, we discuss various research challenges and directions of further research around this domain.
OriginalsprogEngelsk
TitelLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Antal sider15
ForlagSpringer Verlag,
Publikationsdato2017
Sider26-40
ISBN (Trykt)9783319570440
DOI
StatusUdgivet - 2017
NavnLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Vol/bind10230 LNCS

ID: 218484460