An Analytics Research for Big Data Graph Analytics: Tools Techniques and Issues, Challenges

In the era of big data, interest in analysis and extraction of information from massive data graphs is increasing rapidly. This paper examines the field of graph analytics from a query processing point of view. Whether it be determination of shortest paths or finding patterns in a data graph matching a query graph, the issue is to find interesting characteristics or information content from graphs. Many of the associated problems can be abstracted to problems on paths or problems on patterns. Unfortunately, seemingly simple problems, such as finding patterns in a data graph matching a query graph are surprisingly difficult (e.g., dual simulation has cubic complexity and sub graph isomorphism is NP-hard). In addition, the iterative nature of algorithms in this field makes the simple MapReduce style of parallel and distributed processing less effective. 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.