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K Nearest Neighbour Joins for Big Data on MapReduce: a Theoretical and Experimental Analysis

K Nearest Neighbour Joins for Big Data on MapReduce: a Theoretical and Experimental Analysis

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 K Nearest Neighbour Joins for Big Data on MapReduce: a Theoretical and Experimental Analysis

 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 computational 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 distributed large scale data processing. Although these works provide different solutions to the same problem, each one has particular constraints and properties. In this paper, we compare the different existing approaches for computing kNN on MapReduce, first theoretically, and then by performing an extensive experimental evaluation.


 


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  • Model: PROJ7084
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This product was added to our catalog on Friday 02 June, 2017.

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