Big Graph Processing in MapReduce

作为大数据处理的一个大分支，大图处理在工业界和学术界都变得越来越重要，因为图具有强大的表达能力来模拟现实世界中实体之间的复杂关系。这个项目将找到使用MapReduce处理大图形的高度可扩展的解决方案。MapReduce是一个大数据处理框架，在处理结构化查询语言风格的查询时具有可扩展性，但在处理大图形时仍然是开放的。在这个项目中研究的大多数问题都是基本的图问题，这些问题在MapReduce中没有得到很好的研究。该项目将进一步提高大图处理技术的水平，对科学和社会都有重要意义。

As a large branch of big data processing, big graph processing is becoming increasingly important in both industry and academia, due to the large expressive power of graphs to model complex relationships among entities in the real world. This project will find highly scalable solutions to process big graphs using MapReduce. MapReduce is a big data processing framework that is shown to be scalable to handle structured query language-styled queries but is still open when it is used to process big graphs. Most of the problems studied in this project are fundamental graph problems that are not well studied in MapReduce. This project will enhance big graph processing which is beneficial for both science and society.