The Linked Data Benchmark Council (LDBC): Driving competition and collaboration in the graph data management space

TL;DR

The paper discusses the LDBC's efforts to develop standard benchmarks for graph data management, fostering competition and collaboration to improve system performance and capabilities.

Contribution

It introduces the LDBC organization, its decade-long work on benchmarks, graph schemas, and query languages to advance graph data management.

Findings

Development of standard benchmarks for graph data systems

Promotion of vendor competition and collaboration

Research on graph schemas and query languages

Abstract

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.