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 socialmedias like face book, twitter and also large amount of financial reports are collected every day. Butthere is no assurance that this data are free from noise. These data are always incomplete andinconsistent which will leads to uncertainty. If the amount of data increases so as the uncertaintyarising from the data also increases. This uncertainty may arise surrounding the 5 big datacharacteristics (5v’s namely variability, viscosity, validity, and viability and volume). Also the latestartificial intelligence techniques like machine learning, natural language processing, andcomputational intelligence provides more efficient and scalable data than the traditional big dataanalytic techniques. But these techniques also suffer from uncertainty in data sets. So to overcome thissummary statistics method is followed which will calculate mean and variance from the distributedsample data thereby reducing the uncertainty caused due large amount of vague data. Alsodistribution based decision tree method is also applied which reduces the problem of uncertainty to ahug