Comparative Study on Different Data Fusion Techniques

Technological advances in sensors and many other communication areas, a large amount of data is continuously created every day at unprecedented scale. The diversity of data sets from different sources and domains are being faced in the big data era. Since the data is in different formats and different structures, how to unlock the quality information/knowledge from the multiple data sets is the most challenging task in big data. And this can be solved by Data fusion method. Data fusion is one of the efficient methods to extract the quality information from the data sets by combing the different data sets than any other individual data sources. The success of the big data is based on the ability to extract the meaningful information from such massive amount of information through data analytics to attain the efficient decision making. A timely fusion and analysis of big data provides a highly reliable, efficient and accurate decision making on the data sets. Data Fusion has achieved numerous successes in many areas such as Image recognition, Biometrics, Natural language processing, Healthcare etc. The key objective of the paper is to provide the overview of various literature on the data fusion such as different methodologies of data fusion and the challenges and opportunities are discussed under each method.