Machine Learning Techniques and Frameworks for Heterogeneous Data Fusion in Big Data Analytics

Big Data systems are now facing many challenges. Variety of data or heterogeneous nature of the data is one of the main challenges associated with Big Data. It is observed that, the data fusion can efficiently deal with this challenge. Data fusion aims at optimizing and combining the data from different sources so that the data can provide more value for decision making. Many applications can gain from data fusion. As big data applications are on rise, data fusion should become an integral part of the big data application life cycle. In this paper a review of the different data fusion techniques and their applications is provided. The main focus is to review the machine learning techniques that are applied in data fusion. This paper also covers the various frameworks for implementing machine learning. Towards the end of the paper, the recent trends in data fusion are also discussed.