Paper 5

kdANN+: A Rapid AkNN Classier for Big Data

Authors: Nikolaos Nodarakis, Evaggelia Pitoura, Spyros Sioutas, Athanasios Tsakalidis, Dimitrios Tsoumakos, and Giannis Tzimas

Volume 24 (2016)

Abstract

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. In this work, we propose a novel method for classifying multidimensional data using an AkNN algorithm in the MapReduce framework. Our approach exploits space decomposition techniques for processing the classification procedure in a parallel and distributed manner. To our knowledge, we are the first to study the kNN classification of multidimensional objects under this perspective. Through an extensive experimental evaluation we prove that our solution is efficient, robust and scalable in processing the given queries.