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Big data has received a significant amount of attention from both the academia and industry since the US government announced the Big Data Initiative in 2012. Big data is featured swith a number of Vs include high volume, velocity, variety and veracity. The purpose of the special issue is to collate a selection of representative articles that were primarily presented at the 11th International Conference onNatural Computation (ICNC) on 15–17 August 2015 in Zhangjiajie, China. The special issue was also made open to public submissions for a wide inclusion. MapReduce [1–3] has become a major computing model in support of big data applications especially in dealing with data of huge volumes in an offline mode. As a result, MapReduce has been widely employed in parallelization of data intensive applications. In [4], Wang et al. employ MapReduce to parallel a spatio-temporal convolutional neural network for automatic recognition of action events in surveillance videos. The Map and Reduce processes are implemented on a single computer with multiple CPU cores. Liu et al. [5] further evaluate the performance of MapReduce in computation in comparison with Spark [6], an in-memory computing technology which can be deployed on MapReduce. A back propagation neural network (BPNN) is parallelized withMapReduce and Spark respectively and the speedup of the parallel BPNN using Spark is significantly faster than the one parallelized with MapReduce. Alternatively, Phan et al. [7] present an approach to speeding up the computation of neural networks inspired by the parallel circuits found in the human retina. Wu

Guest Editorial: The Parallel Storage, Processing and Analysis for Big Data