Selective Provenance for Datalog Programs Using Top-K Queries
Summary: Top-k how-provenance for Datalog via a tree-pattern selection and ranking over derivations. An instrumented, bottom-up evaluation generates only relevant provenance, achieving polynomial data complexity and linear-time top-k construction, with scalable experiments. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Daniel Deutch
- 2. Amir Gilad
- 3. Yuval Moskovitch
Incoming Citations (Sorted by Pagerank)
Showing 10 of 10 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,851 | Provenance for Natural Language Queries | 2017 | VLDB | 5.8768322e-05 |
| 5,209 | Explaining Outputs in Modern Data Analytics | 2016 | VLDB | 5.629362e-05 |
| 6,975 | NLProveNAns: Natural Language Provenance for Non-Answers | 2018 | VLDB | 4.8772572e-05 |
| 7,066 | On Multiple Semantics for Declarative Database Repairs | 2020 | SIGMOD | 4.8445108e-05 |
| 7,482 | Provenance-Enabled Explainable AI | 2024 | SIGMOD | 4.7180617e-05 |
| 8,394 | Hypothetical Reasoning via Provenance Abstraction | 2019 | SIGMOD | 4.527807e-05 |
| 9,622 | NLProv: Natural Language Provenance | 2016 | VLDB | 4.3163112e-05 |
| 10,147 | Causal Explanations for Disparate Trends: Where and Why? | 2026 | SIGMOD | 4.1945683e-05 |
| 11,681 | Datalignment: Ontology Schema Alignment Through Datalog Containment | 2019 | VLDB | 4.1945683e-05 |
| 11,733 | Provenance Summaries for Answers and Non-Answers | 2018 | VLDB | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 14 of 14 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,145 | Evaluating Top-k Queries with Inconsistency Degrees | 2020 | VLDB | 4.5761263e-05 |
| 9,813 | Datalog with First-Class Facts | 2025 | VLDB | 4.2783272e-05 |
| 8,960 | Computing How-Provenance for SPARQL Queries via Query Rewriting | 2021 | VLDB | 4.4206222e-05 |
| 1,106 | Provenance for Aggregate Queries | 2011 | PODS | 0.0001398766 |
| 8,394 | Hypothetical Reasoning via Provenance Abstraction | 2019 | SIGMOD | 4.527807e-05 |
| 4,851 | Provenance for Natural Language Queries | 2017 | VLDB | 5.8768322e-05 |
| 6,186 | On Provenance Minimization | 2011 | PODS | 5.166082e-05 |
| 2,173 | Querying Data Provenance | 2010 | SIGMOD | 9.3676609e-05 |
| 8,125 | The Complexity of Why-Provenance for Datalog Queries | 2024 | PODS | 4.5797807e-05 |
| 10,922 | Below and Above Why-Provenance for Datalog Queries | 2024 | PODS | 4.1945683e-05 |