论文标题
基准图图数据管理和处理系统：调查
Benchmarking Graph Data Management and Processing Systems: A Survey
论文作者
论文摘要
对于图形数据系统的可扩展，代表性和广泛采用的基准的开发一直是一个问题，几十年来一直在寻求答案。我们对现有的有关图形数据管理和处理基准的现有文献进行了深入研究，涵盖了过去15年中开发的20种不同基准。我们将基准分为三个领域，重点介绍了图形处理系统，图形数据库基准和带有图形处理工作负载的BigData基准测试。 This systematic approach allows us to identify multiple issues existing in this area, including i) few benchmarks exist which can produce high workload scenarios, ii) no significant work done on benchmarking graph stream processing as well as graph based machine learning, iii) benchmarks tend to use conventional metrics despite new meaningful metrics have been around for years, iv) increasing number of big data benchmarks appear with graph processing workloads.遵循这些观察结果，我们通过描述图形数据系统基准测试的未来研究的关键挑战来结束调查。
The development of scalable, representative, and widely adopted benchmarks for graph data systems have been a question for which answers has been sought for decades. We conduct an in-depth study of the existing literature on benchmarks for graph data management and processing, covering 20 different benchmarks developed during the last 15 years. We categorize the benchmarks into three areas focusing on benchmarks for graph processing systems, graph database benchmarks, and bigdata benchmarks with graph processing workloads. This systematic approach allows us to identify multiple issues existing in this area, including i) few benchmarks exist which can produce high workload scenarios, ii) no significant work done on benchmarking graph stream processing as well as graph based machine learning, iii) benchmarks tend to use conventional metrics despite new meaningful metrics have been around for years, iv) increasing number of big data benchmarks appear with graph processing workloads. Following these observations, we conclude the survey by describing key challenges for future research on graph data systems benchmarking.
