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M2Bench: A Database Benchmark for Multi-Model Analytic Workloads

Summary: Introduces M2Bench, a focused benchmark for multi-model DBMSs covering relational, document, property-graph and notably array models, with workloads modeled on real-world tasks that involve at least two data models. Evaluates polyglot systems to reveal per-model performance and trade-offs. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13515
Venue
VLDB
Year
2023
Pagerank
5.4665936e-05
Overall Rank
8,208 | 43.69%
DOI
10.14778/3574245.3574259

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{kim_vldb23,
        title = {{M2Bench: A Database Benchmark for Multi-Model Analytic Workloads}},
        author = {Kim, Bogyeong and Koo, Kyoseung and Enkhbat, Undraa and Kim, Sohyun and Kim, Juhun and Moon, Bongki},
        journal = {PVLDB},
        series = {{VLDB} '23},
        volume = {16},
        number = {4},
        pages = {747--759},
        doi = {10.14778/3574245.3574259},
        url = {https://doi.org/10.14778/3574245.3574259},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

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Rank Citing Paper Year Venue Pagerank
9,766 On Efficient Large Sparse Matrix Chain Multiplication 2024 SIGMOD 5.2214067e-05
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Outgoing Citations (Sorted by Pagerank)

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