Big Data Processing Based on Machine Learning for Multi-user Environments

Many sources of data yield non-structured data like the Internet of things (IoT), geospatial data, E-commerce, social media, and scientific research that is not appropriate in to traditional, structured warehouses. Nowadays, sophisticated analytical techniques allow companies to obtain perspicacity from data with earlier unachievable levels of accuracy and speed. Real-time analytics for big data is the capability to achieve the most suitable decisions and get significant actions at the best time. First, we present a survey of processing the big data (BD) in real time (RT) and focus on its challenges. Then, we propose an algorithm to handle BD by integration with machine learning operations in multi-user environment optimization operations, reduce maintenance costs and better speed of fault detector and provide common operations necessary to process unstructured information. There are important conditions that have been taken into a concern to guarantee the quality of services (QoS) and transmission velocity and ensure the system’s physical time synchronization and the correctness of the data processing.