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.

Proceedings of the VLDB Endowment
Database Architectures

Szárnyas, G, Bebee, B, Birler, A, Deutsch, A, Fletcher, G, Gabb, H.A, … Boncz, P.A. (2023). The Linked Data Benchmark Council (LDBC): Driving competition and collaboration in the graph data management space. In Proceedings of the VLDB Endowment.