Unlock Different V`s of Big Data for Analytics - Dhamodharavadhani, S; Gowri, R (International Journal of Computer Sciences and Engineering, 2025)
This paper aims to review the purpose of the Big Data characteristics, to identify Big Data solutions in different perspective. In 2001, the first three ‘V’ (Volume, Velocity, and Variety) dimensions of Big Data are addressed. Later, V’s like Variability, Veracity, Virality, Visualization and Value were compiled from several sources including IBM, Data Science Central, National Institute of Standards and Technology (NIST) etc.,. Recently, characteristics of Big Data increased to understand and analyze the big data efficiently and effectively. The big data and big data policy can be better revealed by adding more V’s. Addition of more V’s was providential, in the sense that big data first act in response were meet these additional challenges with this massive data. The new V’s are added to the list will provide valuable and most excellent observation over the data. Therefore, this study tries to summarize the available characteristics in the literature to get the better picture about Big Data further. From this, it has been observed that there are more than 54 V’s dimensions (characteristics) like Venue, Vocabulary, Vendible, Validity, Volatility, Verbosity, Vagueness, Vanity, Voracity and so on. These characteristics were emerged to suit different applications and domains. This review results in finding the impacts of V’s on Big data analytics.