Data Collision of incomplete unlabelled ambiguous big data investigation - G.Senthil Velan , D.Syed Ali , T.Yuvarani , P. Dineshkumar , C.Tamilselvi (International Journal of Advanced Science and Technology, 2020)
Nowadays the internet is stormed with huge amount of data. Massive amount of data from social medias like face book, twitter and also large amount of financial reports are collected every day. But there is no assurance that this data are free from noise. These data are always incomplete and inconsistent which will leads to uncertainty. If the amount of data increases so as the uncertainty arising from the data also increases. This uncertainty may arise surrounding the 5 big data characteristics (5v’s namely variability, viscosity, validity, and viability and volume). Also the latest artificial intelligence techniques like machine learning, natural language processing, and computational intelligence provides more efficient and scalable data than the traditional big data analytic techniques. But these techniques also suffer from uncertainty in data sets. So to overcome this summary statistics method is followed which will calculate mean and variance from the distributed sample data thereby reducing the uncertainty caused due large amount of vague data. Also distribution based decision tree method is also applied which reduces the problem of uncertainty to a hug