Orri Erling, Alex Averbuch, Josep-L. Larriba-Pey, Hassan Chafi, Andrey Gubichev, Arnau Prat, Minh-Duc Pham, Peter Boncz
Paper appears method- or tooling-adjacent to AI workflows with partial ecosystem coverage.
The Linked Data Benchmark Council (LDBC) is now two years underway and has gathered strong industrial participation for its mission to establish benchmarks, and benchmarking practices for evaluating graph data management systems. The LDBC introduced a new choke-point driven methodology for developing benchmark workloads, which combines user input with input from expert systems architects, which we outline. This paper ...
describes the LDBC Social Network Benchmark (SNB), and presents database benchmarking innovation in terms of graph query functionality tested, correlated graph generation techniques, as well as a scalable benchmark driver on a workload with complex graph dependencies. SNB has three query workloads under development: Interactive, Business Intelligence, and Graph Algorithms. We describe the SNB Interactive Workload in detail and illustrate the workload with some early results, as well as the goals for the two other workloads.
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The Linked Data Benchmark Council (LDBC) is now two years underway and has gathered strong industrial participation for its mission to establish benchmarks, and benchmarking practices for evaluating graph data management systems.
ldbc/ldbc_snb_bi is the closest maintained adjacent implementation (Matches contextual method/domain keyword: workload). It is not paper-verified; validate algorithm and evaluation setup against the paper before trusting reported metrics. Community adoption signal: 46 GitHub stars.
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Evidence graph: 3 refs, 3 links.
Utility signals: depth 70/100, grounding 75/100, status medium.
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Strong overlap with paper title keywords · Community adoption signal (183 stars)
Risk flags
Strong overlap with paper title keywords · Community adoption signal (112 stars)
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262
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Tasks
Benchmarking, Computer science, Workload, Benchmark (surveying), Testbed, Scalability, Graph, Distributed computing
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Domains
Computer Vision and Pattern Recognition
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