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)
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Authors
- 1. Efthymia Tsamoura (Samsung)
- 2. Jaehun Lee (Samsung)
- 3. Jacopo Urbani (Vrije Universiteit Amsterdam)
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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