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

Szárnyas, G. and Bebee, B. and Birler, A. and Deutsch, A. and Fletcher, G. and Gabb, H.A. and Gosnell, D. and Green, A. and Guo, Z. and Hare, K.W. and Hidders, Jan and Iosup, A. and Kiryakov, A. and Kovatchev, T. and Li, X. and Libkin, L. and Lin, H. and Luo, X. and Prat-Pérez, A. and Püroja, D. and Qi, S. and van Rest, O. and Steer, B.A. and Szakállas, D. and Tong, B. and Waudby, J. and Wu, M. and Yang, B. and Yu, W. and Zhang, C. and Zhang, J. and Zhou, Y. and Boncz, P. (2024) The Linked Data Benchmark Council (LDBC): driving competition and collaboration in the graph data management space. In: Performance Evaluation and Benchmarking 15th TPC Technology Conference, TPCTC 2023, 28 Aug - 01 Sep 2023, Vancouver, Canada.

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.

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