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A $k$-nearest neighbor ($k$NN) query determines the $k$ nearest points, usingdistance metrics, from a specific location. An all $k$-nearest neighbor(A$k$NN) query constitutes a variation of a $k$NN query and retrieves the $k$nearest points for each point inside a database. Their main usage resonates inspatial databases and they consist the backbone of many location-basedapplications and not only (i.e. $k$NN joins in databases, classification indata mining). So, it is very crucial to develop methods that answer themefficiently. In this work, we propose a novel method for classifyingmultidimensional data using an A$k$NN algorithm in the MapReduce framework. Ourapproach exploits space decomposition techniques for processing theclassification procedure in a parallel and distributed manner. To ourknowledge, we are the first to study the classification of multidimensionalobjects under this perspective. Through an extensive experimental evaluation weprove that our solution is efficient and scalable in processing the givenqueries. We investigate many different perspectives that can affect the totalcomputational cost, such as different dataset distributions, number ofdimensions, growth of $k$ value and granularity of space decomposition andprove that our system is efficient, robust and scalable.

Rapid AkNN Query Processing for Fast Classification of Multidimensional Data in the Cloud