FaDE: More Than a Million What-ifs Per Second
Summary: FaDE compiles provenance into relational evaluation plans (not symbolic expressions) to evaluate deletion/scaling interventions at low latency. With compilation, parallel, incremental and sparse evaluation it yields ~1000x vs IVM, ~10000x vs prior provenance and >1M interventions/sec. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Haneen Mohammed
- 2. Alexander Yao
- 3. Charlie Summers
- 4. Hongbin Zhong
- 5. Gromit Yeuk-Yin Chan
- 6. Subrata Mitra
- 7. Lampros Flokas
- 8. Eugene Wu
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,967 | Please Don't Kill My Vibe: Empowering Agents with Data Flow Control | 2026 | CIDR | 4.1905499e-05 |
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 20 of 20 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
Previous
Page 1 / 1
Next
Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,722 | What If: Causal Analysis with Graph Databases | 2025 | VLDB | 4.1905499e-05 |
| 10,177 | InferF: Declarative Factorization of AI/ML Inferences over Joins | 2026 | SIGMOD | 4.1905499e-05 |
| 7,666 | Fast Detection of Denial Constraint Violations | 2022 | VLDB | 4.6792751e-05 |
| 722 | FAD, a Powerful and Simple Database Language | 1987 | VLDB | 0.00017505795 |
| 12,150 | Demonstration of the FDB Query Engine for Factorised Databases | 2012 | VLDB | 4.1905499e-05 |
| 3,492 | Fauce: Fast and Accurate Deep Ensembles with Uncertainty for Cardinality Estimation | 2021 | VLDB | 7.0435484e-05 |
| 3,088 | FDB: A Query Engine for Factorised Relational Databases | 2012 | VLDB | 7.5940302e-05 |
| 8,392 | Hypothetical Reasoning via Provenance Abstraction | 2019 | SIGMOD | 4.5234647e-05 |
| 9,702 | CaJaDE: Explaining Query Results by Augmenting Provenance with Context | 2022 | VLDB | 4.2964668e-05 |
| 7,556 | Interactive Query Explanations Using Fine Grained Provenance | 2022 | SIGMOD | 4.7072603e-05 |