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• W3000656992 abstract "The Social Network Benchmark's Business Intelligence workload (SNB BI) is a comprehensive graph OLAP benchmark targeting analytical data systems capable of supporting graph workloads. This paper marks the finalization of almost a decade of research in academia and industry via the Linked Data Benchmark Council (LDBC). SNB BI advances the state-of-the art in synthetic and scalable analytical database benchmarks in many aspects. Its base is a sophisticated data generator, implemented on a scalable distributed infrastructure, that produces a social graph with small-world phenomena, whose value properties follow skewed and correlated distributions and where values correlate with structure. This is a temporal graph where all nodes and edges follow lifespan-based rules with temporal skew enabling realistic and consistent temporal inserts and (recursive) deletes. The query workload exploiting this skew and correlation is based on LDBC's choke point-driven design methodology and will entice technical and scientific improvements in future (graph) database systems. SNB BI includes the first adoption of parameter curation in an analytical benchmark, a technique that ensures stable runtimes of query variants across different parameter values. Two performance metrics characterize peak single-query performance (power) and sustained concurrent query throughput. To demonstrate the portability of the benchmark, we present experimental results on a relational and a graph DBMS. Note that these do not constitute an official LDBC Benchmark Result - only audited results can use this trademarked term." @default.
• W3000656992 created "2020-01-23" @default.
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• W3000656992 date "2022-12-01" @default.
• W3000656992 modified "2023-10-16" @default.
• W3000656992 title "The LDBC Social Network Benchmark" @default.
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• W3000656992 doi "https://doi.org/10.14778/3574245.3574270" @default.
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