Sayyed Aziz Ahmed Dafedar1 , Dr. Manoj Eknath Patil2

Research Scholar1, Research Guide2

1,2Department of Computer Science & Engineering, Dr.A.P.J.Abdul Kalam University, Indore(M.P)

aziz.dafi@gmail.com1 , mepatil@gmail.com2

Abstract- Recent developments in the Internet of Things (IoT)—a term that encompasses people, sensors, and mobile devices—have led to vastly improved network size, intelligence, and responsiveness. A primary reason for this is the proliferation of internet-enabled mobile devices. The network is dynamic in space and time if and when its topology shifts and the properties of its components evolve over time. Keeping up with the construction and evolution of dynamic networks is becoming increasingly crucial. The statistical features of networks can be examined via data mining methods. While effective, these methods are prohibitively expensive to implement on big, dynamic networks. People can only take in so much information at once, therefore traditional methods of displaying data like as animations and sequences of snapshot graphs aren’t very helpful. We offer a dynamic representation and visualization in networking for big data(DRVN-BD) system that allows users to examine a graph’s components in terms of their physical location and chronological progression. Networks are examined and analysed in real time by this technology. we apply topological dynamics in conjunction with attribute dynamics to increase the spatial and temporal isolation of nodes and edges (e.g., time and locations of connectivity). DRVN-BD is designed to facilitate scalable network analysis of large, dynamic networks. It accomplishes this via built-in data filtering modules, graph views, and statistical dynamic overviews. we demonstrate how DRVN-BD may be used to detect and rank anomalies in real-world dynamic networks, such as those used in computer communication. it is best to combine analytical and visualisation processes. In this research work, we demonstrate both time-honored methods of displaying information and modifications to those methods suitable for Big Data analysis. The difficulties of deciphering Big Data are discussed. Recent developments in the methods, tools, and understanding of Big Data visualisation are discussed.

Keywords: Data visualization , Big Data , Dynamic Representation.