G*: A parallel system for efficiently managing large graphs in the cloud

Many of today’s data analytics applications require processing a series of large graphs that represent an evolving network. This paper presents a new parallel system that efficiently supports these applications in the cloud. This system, G*, stores large graphs on servers in a scalable fashion while compressing the graphs based on their commonalities. Unlike traditional database and graph processing systems, G* can efficiently execute complex queries on large graphs using operators that process graph data in parallel. G* speeds up queries on multiple graphs by processing commonalities among graphs once and then sharing the result across relevant graphs. G* provides a set of processing primitives that abstract away the complexity of distributed computation and enable easy and succinct implementation of operators. This paper presents evaluation results that substantiate the unique benefits of G* over traditional database and graph processing systems.