Data Cloud for Distributed Data Mining via Pipelined MapReduce

Distributed data mining (DDM) which often utilizes autonomous agents is a process to extract globally interesting associations, classifiers, clusters, and other patterns from distributed data. As datasets double in size every year, moving the data repeatedly to distant CPUs brings about high communication cost. In this paper, data cloud is utilized to implement DDM in order to move the data rather than moving computation. MapReduce is a popular programming model for implementing data-centric distributed computing. Firstly, a kind of cloud system architecture for DDM is proposed. Secondly, a modified MapReduce framework called pipelined MapReduce is presented. We select Apriori as a case study and discuss its implementation within MapReduce framework. Several experiments are conducted at last. Experimental results show that with moderate number of map tasks, the execution time of DDM algorithms (i.e., Apriori) can be reduced remarkably. Performance comparison between traditional and our pipelined MapReduce has shown that the map task and reduce task in our pipelined MapReduce can run in a parallel manner, and our pipelined MapReduce greatly decreases the execution time of DDM algorithm. Data cloud is suitable for a multitude of DDM algorithms and can provide significant speedups.