Review Paper on Shared and Distributed Memory Parallel Algorithms to Solve Big Data Problems in Biological, Social Network and Spatial Domain Applications

Abstract

World goes towards a based on total computerized, which can deal with the big data. Big data analytics is the most valuable era of this prevention. Such kind of research are go with this. Big data refers to information which cannot be processed and analyzed using traditional approaches and tools, due to 4 V'ssheer Volume, Velocity at which data is received and processed, and data Variety and Veracity. Today efficient processing of this massive data is a significant challenge in the field of computer science. In our research we consume some special kind of sub domain of Big data. One way to achieve such efficient and scalable algorithms is by using shared & distributed memory parallel programming models. We solve five problems that fall into two categories. The first group of problems deals with the issue of community detection. We develop a novel sequential algorithm to accurately detect community structures in biological proteinprotein interaction networks, where a community corresponds with a functional module of proteins. Generally, such sequential algorithms are computationally expensive, which makes them impractical to use for large real world networks. To address this limitation, we develop a new highly scalable Symmetric Multiprocessing (SMP) based parallel algorithm to detect high quality communities in large subsections of social networks like Facebook and Amazon. The second gathering of issues manages the issue of effectively preparing spatial Light Detection and Ranging (LiDAR) information. LiDAR information is generally utilized in woodland and rural yield examines, scene grouping, 3D urban displaying, and so forth.