Big Data based Adaptive Learning and Scope of Automation in Actionable Knowledge

In general, big data can be treated as a tool to extract knowledge from the larger datasets with different data values. Big-data research is being carried out for a long time to extract the data from larger datasets. Generally, some of the devices, sensors generate a humongous amount of data continuously. Hence performing any computational operation or search operation in that bulk data is remaining as a challenging task. At present, the big data researchers are facing challenges such as duplication of data, and identifying the data from one data set to others and also identifying the multidisciplinary data is also a challenging task. When the data is increasing tremendously day by day as a result the server size, capacity and device storage size, RAM etc., should also be improved for deploying fast and efficient results. In this research work, the Big Data Analytics Problem (BDAP) is proposed for offering many solutions such as data deduplication, security, reliability access control etc., and finally and most important it is used for making analysis and decision making.