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Scaling-Up In-Memory Datalog Processing: Observations and Techniques

Summary: Cross-domain evaluation shows Datalog engines’ performance is workload-sensitive across graph analytics and program analysis. RecStep, built atop a parallel single-node relational engine, outperforms prior parallel systems 4–6×, challenging the assumption that relational substrates are unsuitable. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
12185
Venue
VLDB
Year
2019
Pagerank
6.0899186e-05
Overall Rank
5,783 | 60.33%
DOI
10.14778/3311880.3311886

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{fan_vldb19,
        title = {{Scaling-Up In-Memory Datalog Processing: Observations and Techniques}},
        author = {Fan, Zhiwei and Zhu, Jianqiao and Zhang, Zuyu and Albarghouthi, Aws and Koutris, Paraschos and Patel, Jignesh M.},
        journal = {PVLDB},
        series = {{VLDB} '19},
        volume = {12},
        number = {6},
        pages = {695--708},
        doi = {10.14778/3311880.3311886},
        url = {https://doi.org/10.14778/3311880.3311886},
        year = {2019}
}

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