Big Graph Analytics Platforms PDF Download

As graph data becomes ubiquitous in modern computing, developing systems to efficiently process large graphs has gained increasing popularity. There are two major types of analytical problems over large graphs: graph computation and graph mining. Graph computation includes a set of problems that can be represented through liner algebra over an adjacency matrix based representation of the graph. Graph mining aims to discover complex structural patterns of a graph, for example, finding relationship patterns in social media network, detecting link spam in web data. Due to their importance in machine learning, web application and social media, graph analytical problems have been extensively studied in the past decade. Practical solutions have been implemented in a wide variety of graph analytical systems. However, most of the existing systems for graph analytics are distributed frameworks, which suffer from one or more of the following drawbacks: (1) many of the (current and future) users performing graph analytics will be domain experts with limited computer science background. They are faced with the challenge of managing a cluster, which involves tasks such as data partitioning and fault tolerance they are not familiar with; (2) not all users have access to enterprise cluster in their daily development tasks; (3) distributed graph systems commonly suffer from large startup and communication overhead; and (4) load balancing in a distributed system is another major challenge. Some graph algorithms have dynamic working sets and and it is thus hard to distribute the workload appropriately before the execution. In this dissertation, we identify three categories of graph workloads for which single-machine systems are more suitable than distributed systems: (1) analytical queries that do not need exact answers; (2) program analysis tasks that are widely used to find bugs in real-world software; and (3) graph mining algorithms that are important for many information-retrieval tasks. Based on these observations, we have developed a set of single-machine graph systems to deliver efficiency and scalability specifically for these workloads. In particular, this dissertation makes the following contributions. The first contribution is the design and implementation of a single-machine graph query system named GraphQ, which divides a large graph into partitions and merges them with the guidance from an abstraction graph. By using multiple levels of abstraction, it can quickly rule out infeasible solutions and identify mergeable partitions. GraphQ uses the memory capacity as a budget and tries its best to find solutions before exhausting the memory, making it possible to answer analytical queries over very large graphs with resources affordable to a single PC. The second contribution is the design and implementation of Graspan, a single-machine, disk-based graph processing system tailored for interprocedural static analyses. Given a program graph and a grammar specification of an analysis, Graspan uses an edge-pair centric computation model to compute dynamic transitive closures on very large program graphs. With the help of novel graph processing techniques, we turn sophisticated code analyses into scalable Big Graph analytics. The third contribution of this dissertation is a single-machine, out-of-core graph mining system, called RStream, which leverages disk support to support efficient edge streaming for mining very large graphs. RStream employs a rich programming model that exposes relational algebra for developers to express a wide variety of mining tasks and implements a runtime engine that delivers efficiency with tuple streaming. In conclusion, this dissertation attempts to explore the opportunities of building single-machine graph systems for scenarios where distributed systems do not work well. Our experimental results demonstrate that the techniques proposed in this dissertation can efficiently solve big graph analytical problems on a single consumer PC. We hope that these promising results will encourage future work to continue building affordable single-machine systems for a rich set of datasets and analytical tasks.