Approximate Summaries for Why and Why-not Provenance
Summary: Proposes approximate summarization of why/why-not provenance via pattern encodings to compress provenance. Adds sampling for scalable capture and concise, informative summaries on large datasets, balancing informativeness, conciseness, and completeness. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Seokki Lee
- 2. Bertram Lud e4scher
- 3. Boris Glavic
Incoming Citations (Sorted by Pagerank)
Showing 8 of 8 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,706 | Putting Things into Context: Rich Explanations for Query Answers using Join Graphs | 2021 | SIGMOD | 5.3633001e-05 |
| 5,918 | Banzhaf Values for Facts in Query Answering | 2024 | SIGMOD | 5.2688869e-05 |
| 8,154 | Why Not Match: On Explanations of Event Pattern Queries | 2021 | SIGMOD | 4.5708988e-05 |
| 8,886 | Provenance-based Data Skipping | 2022 | VLDB | 4.4237386e-05 |
| 10,147 | Causal Explanations for Disparate Trends: Where and Why? | 2026 | SIGMOD | 4.1905499e-05 |
| 10,269 | Database Views as Explanations for Relational Deep Learning | 2026 | VLDB | 4.1905499e-05 |
| 10,429 | Unified Lineage System: Tracking Data Provenance at Scale | 2025 | SIGMOD | 4.1905499e-05 |
| 10,957 | Counterfactual Explanation at Will, with Zero Privacy Leakage | 2024 | SIGMOD | 4.1905499e-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 |
|---|---|---|---|---|
| 10,926 | Below and Above Why-Provenance for Datalog Queries | 2024 | PODS | 4.1905499e-05 |
| 8,129 | The Complexity of Why-Provenance for Datalog Queries | 2024 | PODS | 4.5753896e-05 |
| 11,474 | On Optimizing the Trade-off between Privacy and Utility in Data Provenance | 2021 | SIGMOD | 4.1905499e-05 |
| 2,903 | Data Provenance at Internet Scale: Architecture, Experiences, and the Road Ahead | 2017 | CIDR | 7.9403877e-05 |
| 8,725 | OneProvenance: Efficient Extraction of Dynamic Coarse-Grained Provenance From Database Query Event Logs | 2023 | VLDB | 4.453957e-05 |
| 6,187 | On Provenance Minimization | 2011 | PODS | 5.1611195e-05 |
| 653 | On the Provenance of Non-Answers to Queries over Extracted Data | 2008 | VLDB | 0.00018616975 |
| 4,853 | Provenance for Natural Language Queries | 2017 | VLDB | 5.8711821e-05 |
| 8,392 | Hypothetical Reasoning via Provenance Abstraction | 2019 | SIGMOD | 4.5234647e-05 |
| 11,740 | Provenance Summaries for Answers and Non-Answers | 2018 | VLDB | 4.1905499e-05 |