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Solutions for Processing K Nearest Neighbor Joins for Massive Data on MapReduce

Abstract : Given a point p and a set of points S, the kNN operation finds the k closest points to p in S. It is a compu-tational intensive task with a large range of applications such as knowledge discovery or data mining. However, as the volume and the dimension of data increase, only distributed approaches can perform such costly operation in a reasonable time. Recent works have focused on implementing efficient solutions using the MapReduce programming model because it is suitable for large scale data processing. Also, it can easily be executed in a distributed environment. Although these works provide different solutions to the same problem, each one has particular constraints and properties. There is no readily available comparison to help users choose the one most appropriate for their needs. This is the problem we address in this work. Firstly, we show that all kNN implementations go through a common workflow, which we use as a basis for classification. Secondly, we describe precisely the different techniques published so far. And lastly, we provide a set of objective criteria that can be used to make informed decisions.
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Contributor : Justine Rochas <>
Submitted on : Monday, January 12, 2015 - 1:34:08 PM
Last modification on : Tuesday, January 12, 2021 - 8:44:01 AM
Long-term archiving on: : Thursday, September 10, 2015 - 11:40:52 PM


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  • HAL Id : hal-01097337, version 1


Ge Song, Justine Rochas, Fabrice Huet, Frédéric Magoulès. Solutions for Processing K Nearest Neighbor Joins for Massive Data on MapReduce. 23rd Euromicro International Conference on Parallel, Distributed and Network-based Processing, Mar 2015, Turku, Finland. ⟨hal-01097337⟩



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