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Abstract. MapReduce has become an increasingly popular framework for large-scale data processing. However, complex operations such as joins are quite expensive and require sophisticated techniques. In this paper, we review state-of-the-art strategies for joining several relations in a MapReduce environment and study their extension with filter-based approaches. The general objective of filters is to eliminate non-matching data as early as possible in order to reduce the I/O, communication and CPU costs. We examine the impact of systematically adding filters as early as possible in MapReduce join algorithms, both analytically with cost models and practically with evaluations. The study covers binary joins, multi-way joins and recursive joins, and addresses the case of large inputs that gives rise to the most intricate challenges.

Nowadays, big data is a revolution that transforms conventional enterprises into data-driven organizations in which knowledge discovered from big data will be integrated into traditional knowledge to improve decision-making and to facilitate organizational learning. Consequently, a major concern is how to evolve current knowledge management systems, which are confronted with a various and unprecedented amount of data, resulting from different data sources. Therefore, a new generation of knowledge management systems is required for exploring and exploiting big data as well as for facilitating the knowledge co-creation between the society and its business environment to foster innovation. This article proposes a service-oriented architecture for elaborating a new generation of big data-driven knowledge management systems to help enterprises to promote knowledge co-creation and to obtain more business value from big data. The proposed architecture is presented based on the principles of design science research and its evaluation uses the analytical evaluation method.

Brain haemorrhage is a critical problem with the high mortality rate that is typically diagnosed based on MRI/CT images. A lot of research is gaining attention recently for its high performance in image recognition of brain haemorrhage. In this paper, we propose a new approach, which can automatically detect, diagnose and classify of brain haemorrhages. Our proposed method focuses on analysing brain haemorrhage regions from images of brain haemorrhage. We rely on HU values to detect haemorrhage regions and determine the bleeding time of brain haemorrhages. It is useful for supporting doctors in timely treatment. Our experimental results show that the accuracy of detection of brain haemorrhages is 100% and the classification of brain haemorrhages achieves the accuracy of 93.33%.