FlowLog: Efficient and Extensible Datalog via Incrementality
Summary: FlowLog separates recursive control from per-rule relational plans, combining Datalog-specific optimization with reusable database execution primitives. Built on Differential Dataflow, it supports batch/incremental workloads and recursion-aware Boolean specialization. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Hangdong Zhao (Microsoft)
- 2. Zhenghong Yu (University of Wisconsin)
- 3. Srinag Rao (University of Wisconsin)
- 4. Simon Frisk (University of Wisconsin)
- 5. Zhiwei Fan (Meta)
- 6. Paraschos Koutris (University of Wisconsin)
BibTeX Citation
@article{zhao_vldb26,
title = {{FlowLog: Efficient and Extensible Datalog via Incrementality}},
author = {Zhao, Hangdong and Yu, Zhenghong and Rao, Srinag and Frisk, Simon and Fan, Zhiwei and Koutris, Paraschos},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {3},
pages = {361--374},
doi = {10.14778/3778092.3778098},
url = {https://doi.org/10.14778/3778092.3778098},
year = {2026}
}Incoming Citations (Sorted by Pagerank)
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,135 | I Can't Believe It's Not Yannakakis: Pragmatic Bitmap Filters in Microsoft SQL Server | 2026 | CIDR | 5.093636e-05 |
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