大规模存储和分析历史图形数据
Storing and Analyzing Historical Graph Data at Scale
摘要：
迄今为止，大规模图分析的工作主要集中在对图快照的静态特性的研究上。然而，实体间交互的静态视图通常是对几种复杂现象的过度简化，如流行病的传播、信息扩散、在线社区的形成等。能够找到时间交互模式、可视化图属性的演变，甚至只是简单地比较它们在时间上的变化，为图的推理增加了重要价值。然而，由于缺乏底层的数据管理支持，今天的分析师不得不手动导航处理大型演变图所增加的时间复杂性。在本文中，我们提出了一个系统，称为历史图形存储，使用户能够存储大量的历史图形数据，并表示和运行复杂的时间图分析任务对该数据。它由两个关键组件组成：时间图索引（TGI），以分区和分布式方式压缩存储大量的历史图演化数据;它支持检索过去任何时间点的图快照或单个节点或邻域的演化历史;和基于Spark的时态图分析框架（TAF），用于表达复杂的时态分析任务，并以高效和可扩展的方式执行它们。我们的实验表明，我们的系统的高效存储，检索和分析在各种各样的查询大量的历史图数据。
英文摘要：
The work on large-scale graph analytics to date has largely focused on the study of static properties of graph snapshots. However, a static view of interactions between entities is often an oversimplification of several complex phenomena like the spread of epidemics, information diffusion, formation of online communities}, and so on. Being able to find temporal interaction patterns, visualize the evolution of graph properties, or even simply compare them across time, adds significant value in reasoning over graphs. However, because of lack of underlying data management support, an analyst today has to manually navigate the added temporal complexity of dealing with large evolving graphs. In this paper, we present a system, called Historical Graph Store, that enables users to store large volumes of historical graph data and to express and run complex temporal graph analytical tasks against that data. It consists of two key components: a Temporal Graph Index (TGI), that compactly stores large volumes of historical graph evolution data in a partitioned and distributed fashion; it provides support for retrieving snapshots of the graph as of any timepoint in the past or evolution histories of individual nodes or neighborhoods; and a Spark-based Temporal Graph Analysis Framework (TAF), for expressing complex temporal analytical tasks and for executing them in an efficient and scalable manner. Our experiments demonstrate our system's efficient storage, retrieval and analytics across a wide variety of queries on large volumes of historical graph data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
