@inbook{441936340a9e40928f72f6d0e71da570,
title = "(A)kNN query processing on the cloud: A survey",
abstract = "{\textcopyright} 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.",
keywords = "Big data, MapReduce, Nearest neighbour, NoSQL, Query processing",
author = "Nikolaos Nodarakis and Angeliki Rapti and Spyros Sioutas and Tsakalidis, {Athanasios K.} and Dimitrios Tsolis and Giannis Tzimas and Yannis Panagis",
year = "2017",
doi = "10.1007/978-3-319-57045-7_3",
language = "English",
isbn = "9783319570440",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag,",
pages = "26--40",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
}