The emergence of big data and the potential to undertake complex analysis of very large data sets is, essentially, a consequence of recent advances in the technology that allow this. The development of cloud computing over the last few years represents the single most important contributor to the big data trend, with cloud infrastructure such as compute, storage and analytical tools and apps now widely available. The convergence of big data and cloud computing are having far reaching implications that indeed are changing the world. MapReduce, a widely-adopted parallel and distributed programming paradigm for processing large-scale data sets, becomes much more powerful, scalable, elastic and cost-effective when integrated in cloud systems as it can benefits from the salient characteristics of cloud computing. Based on the MapReduce paradigm and other relevant techniques like HDFS, a series of applications and higher level platforms such as Hadoop, Hive, Twister, Spark, Pregel, to name a few, have been proposed and developed. MapReduce and the emerging tools in cloud are ideal for enterprises with large data centres and scientific communities to address the challenges posed by big data applications. The MapReduce paradigm itself, emerging MapReduce based big data tools and applications, and big data infrastructure such as cloud systems are evolving fast, and therefore need extensive investigations from various research communities. This symposium aims at providing a forum for researchers, practitioners and developers from different background areas such as cloud computing, distributed computing, large-scale data management and database areas to exchange the latest experience, research ideas and synergic research and development on fundamental issues and applications about MapReduce, MapReduce based platforms and emerging big data infrastructure. The symposium solicits high quality research results in all related areas.