A bibliometric analysis and cutting-edge overview on fuzzy techniques in Big Data. (June 2020)

• Record Type: Journal Article
• Authors: Shukla, Amit K.; Muhuri, Pranab K.; Abraham, Ajith
• Is Part Of: Engineering applications of artificial intelligence. Volume 92(2020)
• Publication Date: 2020-06
• Subjects: Big data -- Fuzzy sets -- Type-2 fuzzy sets -- Bibliometric study -- Web of science -- Scopus; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2020.103625
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Over the last few years, Big Data has gained a tremendous attention from the research community. The data being generated in huge quantity from almost every field is unstructured and unprocessed. Extracting knowledge base and useful information from the big raw data is one of the major challenges, present today. Various computational intelligence and soft computing techniques have been proposed for efficient big data analytics. Fuzzy techniques are one of the soft computing approaches which can play a very crucial role in current big data challenges by pre-processing and reconstructing data. There is a wide spread application domains where traditional fuzzy sets (type-1 fuzzy sets) and higher order fuzzy sets (type-2 fuzzy sets) have shown remarkable outcomes. Although, this research domain of "fuzzy techniques in Big Data" is gaining some attention, there is a strong need for a motivation to encourage researchers to explore more in this area. In this paper, we have conducted bibliometric study on recent development in the field of "fuzzy techniques in big data". In bibliometric study, various performance metrics including total papers, total citations, and citation per paper are calculated. Further, top 10 of most productive and highly cited authors, discipline, source journals, countries, institutions, and highly influential papers are also evaluated. Later, a comparative analysis is performed on the fuzzy techniques in big data after analysing the most influential works in this field.