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Probabilistic Reasoning at Scale: Trigger Graphs to the Rescue

Summary: Extends Trigger Graphs to probabilistic reasoning with possible-world semantics while avoiding lineage materialization. LTGs enable scalable probabilistic DB reasoning by grouping derivations, outperforming engines and handling larger uncertain data. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6604
Venue
SIGMOD
Year
2023
Pagerank
5.093636e-05
Overall Rank
11,385 | 21.89%
DOI
10.1145/3588719

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BibTeX Citation

@inproceedings{tsamoura_sigmod23,
        title = {{Probabilistic Reasoning at Scale: Trigger Graphs to the Rescue}},
        author = {Tsamoura, Efthymia and Lee, Jaehun and Urbani, Jacopo},
        series = {{SIGMOD} '23},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3588719},
        url = {https://dl.acm.org/doi/10.1145/3588719},
        year = {2023}
}

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