Graph data management is instrumental for several use cases such as recommendation, root cause analysis, financial fraud detection, and enterprise knowledge representation. Efficiently supporting these use cases yields a number of unique requirements, including the need for a concise query language and graph-aware query optimization techniques. The goal of the Linked Data Benchmark Council (LDBC) is to design a set of standard benchmarks that capture representative categories of graph data management problems, making the performance of systems comparable and facilitating competition among vendors. LDBC also conducts research on graph schemas and graph query languages. This paper introduces the LDBC organization and its work over the last decade.

翻译：图数据管理在推荐系统、根因分析、金融欺诈检测以及企业知识表示等多个应用场景中发挥着关键作用。高效支持这些应用场景需要满足一系列独特需求，包括简洁的查询语言和图感知查询优化技术。链接数据基准委员会（LDBC）的目标是设计一套能够涵盖代表性图数据管理问题的标准基准，从而使得不同系统的性能具有可比性，并促进供应商之间的竞争。此外，LDBC还开展图模式和图查询语言的研究。本文介绍了LDBC组织及其在过去十年中的工作。