Presents corrections to the paper, “K nearest neighbour joins for big data on MapReduce: A theoretical and experimental analysis,” (Song, G., et al), IEEE Trans. Knowl. Data Eng., vol. 28, no. 9, pp. 2376–2392, Sep. 2016.

In order to tackle the development of concurrent and distributed systems, the active object programming model provides a high-level abstraction to program concurrent behaviours. There exists already a variety of active object frameworks targeted at a large range of application domains: modelling, verification, efficient execution. However, among these frameworks, very few consider a multi-threaded execution of active objects. Introducing controlled parallelism within active objects enables overcoming some of their limitations. In this paper, we present a complete framework around the multi-active object programming model. We present it through ProActive, the Java library that offers multi-active objects, and through MultiASP, the programming language that allows the formalisation of our developments. We then show how to compile an active object language with cooperative multi-threading into multi-active objects. This paper also presents different use cases and the development support to illustrate the practical usability of our language. Formalisation of our work provides the programmer with guarantees on the behaviour of the multi-active object programming model and of the compiler.

Given a point$p$and a set of points$S$, the kNN operation finds the$k$closest points to 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. To be able to compare solutions, we identify three generic steps for kNN computation on MapReduce: data pre-processing, data partitioning, and computation. We then analyze each step from load balancing, accuracy, and complexity aspects. Experiments in this paper use a variety of datasets, and analyze the impact of data volume, data dimension, and the value of k from many perspectives like time and space complexity, and accuracy. The experimental part brings new advantages and shortcomings that are discussed for each algorithm. To the best of our knowledge, this is the first paper that compares kNN computing methods on MapReduce both theoretically and experimentally with the same setting. Overall, this paper can be used as a guide to tackle kNN-based practical problems in the context of big data.

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 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.