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

Research output: Chapter in Book/Report/Conference proceedingBook chapterResearchpeer-review

Nikolaos Nodarakis, Angeliki Rapti, Spyros Sioutas, Athanasios K. Tsakalidis, Dimitrios Tsolis, Giannis Tzimas, Yannis Panagis

© 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.
Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number of pages15
PublisherSpringer Verlag,
Publication date2017
Pages26-40
ISBN (Print)9783319570440
DOIs
Publication statusPublished - 2017
SeriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10230 LNCS

    Research areas

  • Big data, MapReduce, Nearest neighbour, NoSQL, Query processing

ID: 218484460