GRADOOP：使用Hadoop的可扩展图形数据管理和分析
GRADOOP: Scalable Graph Data Management and Analytics with Hadoop
摘要：
商业和科学中的许多大数据应用需要管理和分析大量的图形数据。用于图分析的先前方法，诸如图数据库和并行图处理系统（例如，Pregel）要么缺乏足够的可扩展性，要么缺乏灵活性和表现力。因此，我们正在开发一种新的端到端方法，用于基于Hadoop生态系统的图数据管理和分析，称为Gradoop（Hadoop上的图分析）。Gradoop是围绕所谓的扩展属性图数据模型（EPGM）设计的，支持许多不同图形中语义丰富的无模式图形数据。提供了一组高级运算符，用于分析单个图和图的集合。基于这些操作符，我们提出了一个特定领域的语言来定义分析工作流。Gradoop图形存储目前正在使用HBase在Hadoop集群中分布式存储图形数据。Gradoop的初始版本已被用于分析商业智能和社会网络分析的图形数据。
英文摘要：
Many Big Data applications in business and science require the management and analysis of huge amounts of graph data. Previous approaches for graph analytics such as graph databases and parallel graph processing systems (e.g., Pregel) either lack sufficient scalability or flexibility and expressiveness. We are therefore developing a new end-to-end approach for graph data management and analysis based on the Hadoop ecosystem, called Gradoop (Graph analytics on Hadoop). Gradoop is designed around the so-called Extended Property Graph Data Model (EPGM) supporting semantically rich, schema-free graph data within many distinct graphs. A set of high-level operators is provided for analyzing both single graphs and collections of graphs. Based on these operators, we propose a domain-specific language to define analytical workflows. The Gradoop graph store is currently utilizing HBase for distributed storage of graph data in Hadoop clusters. An initial version of Gradoop has been used to analyze graph data for business intelligence and social network analysis.
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