| 811 |
Provenance for Aggregate Queries |
2011 |
PODS |
50 |
0.00013713646 |
| 1,776 |
Putting Lipstick on Pig: Enabling Database-style Workflow Provenance |
2012 |
VLDB |
25 |
9.6661217e-05 |
| 1,953 |
Caravan: Provisioning for What-If Analysis |
2013 |
CIDR |
19 |
9.297314e-05 |
| 2,234 |
Computing the Shapley Value of Facts in Query Answering |
2022 |
SIGMOD |
19 |
8.7839031e-05 |
| 3,671 |
On Probabilistic Fixpoint and Markov Chain Query Languages |
2010 |
PODS |
7 |
7.0929546e-05 |
| 4,697 |
Provenance for Natural Language Queries |
2017 |
VLDB |
8 |
6.4479645e-05 |
| 5,078 |
ShapGraph: An Holistic View of Explanations through Provenance Graphs and Shapley Values |
2022 |
SIGMOD |
7 |
6.2706745e-05 |
| 5,660 |
A Quest for Beauty and Wealth (or, Business Processes for Database Researchers) |
2011 |
PODS |
4 |
6.0306257e-05 |
| 5,809 |
Banzhaf Values for Facts in Query Answering |
2024 |
SIGMOD |
5 |
5.9703658e-05 |
| 6,472 |
On Provenance Minimization |
2011 |
PODS |
4 |
5.7561073e-05 |
| 6,728 |
Selective Provenance for Datalog Programs Using Top-K Queries |
2015 |
VLDB |
10 |
5.6812425e-05 |
| 6,753 |
On Multiple Semantics for Declarative Database Repairs |
2020 |
SIGMOD |
14 |
5.673161e-05 |
| 7,252 |
Hypothetical Reasoning via Provenance Abstraction |
2019 |
SIGMOD |
4 |
5.5583413e-05 |
| 7,339 |
NLProveNAns: Natural Language Provenance for Non-Answers |
2018 |
VLDB |
2 |
5.5337666e-05 |
| 7,531 |
ExplainED: Explanations for EDA Notebooks |
2020 |
VLDB |
4 |
5.4858756e-05 |
| 7,556 |
Querying and Monitoring Distributed Business Processes |
2008 |
VLDB |
1 |
5.4796165e-05 |
| 8,375 |
FEDEX: An Explainability Framework for Data Exploration Steps |
2022 |
VLDB |
11 |
5.3280107e-05 |
| 8,882 |
Advancing Fact Attribution for Query Answering: Aggregate Queries and Novel Algorithms |
2025 |
VLDB |
2 |
5.2439934e-05 |
| 9,630 |
Equivalence-Invariant Algebraic Provenance for Hyperplane Update Queries |
2020 |
SIGMOD |
2 |
5.1363807e-05 |
| 9,827 |
ProvCite: Provenance-based Data Citation |
2019 |
VLDB |
2 |
5.108228e-05 |
| 9,980 |
NLProv: Natural Language Provenance |
2016 |
VLDB |
3 |
5.0870037e-05 |
| 10,288 |
PROPOLIS: Provisioned Analysis of Data-Centric Processes |
2013 |
VLDB |
2 |
5.0323262e-05 |
| 11,903 |
CFDB: Machine Learning Model Analysis via Databases of CounterFactuals |
2022 |
SIGMOD |
0 |
4.9605343e-05 |
| 12,025 |
On Optimizing the Trade-off between Privacy and Utility in Data Provenance |
2021 |
SIGMOD |
1 |
4.9605343e-05 |
| 12,132 |
T-REx: Table Repair Explanations |
2020 |
SIGMOD |
0 |
4.9605343e-05 |
| 12,163 |
MuSe: Multiple Deletion Semantics for Data Repair |
2020 |
VLDB |
1 |
4.9605343e-05 |
| 12,179 |
Personal Insights for Altering Decisions of Tree-based Ensembles over Time |
2020 |
VLDB |
1 |
4.9605343e-05 |
| 12,225 |
Datalignment: Ontology Schema Alignment Through Datalog Containment |
2019 |
VLDB |
0 |
4.9605343e-05 |
| 12,289 |
QuestPro: Queries in SPARQL Through Provenance |
2018 |
VLDB |
0 |
4.9605343e-05 |
| 12,313 |
A Model for Fine-Grained Data Citation |
2017 |
CIDR |
4 |
4.9605343e-05 |
| 12,319 |
Data Citation: a Computational Challenge |
2017 |
PODS |
1 |
4.9605343e-05 |
| 12,552 |
A Provenance Framework for Data-Dependent Process Analysis |
2014 |
VLDB |
1 |
4.9605343e-05 |
| 12,650 |
Mob Data Sourcing |
2012 |
SIGMOD |
0 |
4.9605343e-05 |
| 12,810 |
Navigating in Complex Mashed-Up Applications |
2010 |
VLDB |
0 |
4.9605343e-05 |
| 12,818 |
Optimal Top-K Query Evaluation for Weighted Business Processes |
2010 |
VLDB |
1 |
4.9605343e-05 |
| 12,881 |
Goal-Oriented Web-site Navigation for On-line Shoppers |
2009 |
VLDB |
1 |
4.9605343e-05 |
| 12,914 |
Type Inference and Type Checking for Queries on Execution Traces |
2008 |
VLDB |
4 |
4.9605343e-05 |
| 12,949 |
Querying Web-Based Applications Under Models of Uncertainty |
2008 |
VLDB |
0 |
4.9605343e-05 |
| 13,771 |
Theory and Practice of Provenance |
2022 |
SIGMOD |
0 |
- |