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

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arxiv 2307.04350 v3 pith:KGB77EJ5 submitted 2023-07-10 cs.DB

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

G\'abor Sz\'arnyas , Brad Bebee , Altan Birler , Alin Deutsch , George Fletcher , Henry A. Gabb , Denise Gosnell , Alastair Green
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Zhihui Guo Keith W. Hare Jan Hidders Alexandru Iosup Atanas Kiryakov Tomas Kovatchev Xinsheng Li Leonid Libkin Heng Lin Xiaojian Luo Arnau Prat-P\'erez David P\"uroja Shipeng Qi Oskar van Rest Benjamin A. Steer D\'avid Szak\'allas Bing Tong Jack Waudby Mingxi Wu Bin Yang Wenyuan Yu Chen Zhang Jason Zhang Yan Zhou Peter Boncz
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classification cs.DB
keywords datagraphldbcmanagementquerybenchmarkcasescompetition
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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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  1. Validation of graph databases against PG-Schema

    cs.DB 2026-06 unverdicted novelty 5.0

    PG-Schema validation is NP-complete in combined complexity and PTIME in data complexity; combined complexity becomes PTIME under restricted alternation of type combinations and unions.