Extensions of Fuzzy Sets in Big Data Applications: A Literature Review

Nowadays, with the increase in technological developments and the widespread use of the internet, large amounts of data are produced from many sources, revealing huge and heterogeneous data difficult to process. Therefore, big data having an enormous volume and high velocity of data with complex structures have recently drawn substantial interest from not only academics but also practitioners. While academic researchers focus on understanding the concept, identifying it, and developing related methodologies, companies focus on how to transform the potential of this technology into business values and how they can benefit from this technology. Researchers have proposed new research paradigms by addressing big data more efficiently to guide both literature and businesses on these issues. Fuzzy sets have been accepted as a suitable method to represent and quantify aspects of uncertainty on big data. However, there are very few systematic research reviews that capture the dynamic nature of this issue for both academics and businesses who want to research this topic. Therefore, this study takes into consideration the studies employing fuzzy sets in big data applications. We aim to present a literature review to lead the researches on the existing literature and the most recent advances on big data. A large number of papers employing fuzzy sets in big data applications have been analyzed with respect to some characteristics such as subject area, published journal, publication year, source country, and document type.