Survey Paper For Real-Time Clustering for Big Data Streams

Abstract — Big data is a recent term Appeared that has to define the vey large amount of data that surpass the traditional storage and processing requirements. Each and every growing volume of data generation is the reality. Today we are living in Social networks, smart cities, telephone networks, the internet are hand Reviews some of the data in the modern world and much of this information is discarded due to the high storage space. It Would require Relevant data can be extracted from this large amount of information and to be used to build better cities, offers better services, make predictive analysis, group similar information and many more applications. All of this is possible, due to machine learning and data mining can be found where patterns in the ocean of data generated every Second in order to cope with the volume, velocity and variety of data produced a streaming model has-been Studied. Were analysis of data has to uses low memory and process items only once. Currently we are taking the advance of grid and cloud computing the missing component to help crunch this large amount of data is the power of distributed computing. Stream Processing Engines (SPE) have revealed to be a more flexible and powerful tool for dealing with Big Data. This project merges the concepts of machine learning, streaming model and distributed computing to build a framework for developing, Testing and applying algorithms on large volume of data streams. Keywords