Author :
Dhananjay Kumar Singh
Affiliation : Department of Computer Science &
Engineering National Institute of Technology Silchar Assam
Country :
India
Category : Computer Science & Information Technology
Volume, Issue,
Month, Year
: 6, 9, November, 2016
ABSTRACT
Analyzing interconnection structures among the data through the use of
graph algorithms and graph analytics has been shown to provide tremendous value
in many application domains (like social networks, protein networks,
transportation networks, bibliographical networks, knowledge bases and many
more). Nowadays, graphs with billions of nodes and trillions of edges have
become very common. In principle, graph analytics is an important big data discovery
technique. Therefore, with the increasing abundance of large scale graphs,
designing scalable systems for processing and analyzing large scale graphs has
become one of the timeliest problems facing the big data research community. In
general, distributed processing of big graphs is a challenging task due to
their size and the inherent irregular structure of graph computations. In this
paper, we present a comprehensive overview of the state-of-the-art to better
understand the challenges of developing very high-scalable graph processing
systems. In addition, we identify a set of the current open research challenges
and discuss some promising
directions for future research.
Keyword : Big Data, Big Graph, Graph Processing, Graph
Analytics, Graph Parallel Computing, Distributed Processing, Graph Algorithms
For More Details :
https://airccj.org/CSCP/vol6/csit65611.pdf
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