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FEDEX: An Explainability Framework for Data Exploration Steps
Summary: FEDEX, explainability framework for data exploration, pinpoints interesting rows in each dataframe. Interestingness is each row's contribution to column-level interestingness via diversity and exceptionality; FEDEX uses semantically related sets to explain row correlations.
(summarized by gpt-5-nano on Feb 09 2026)
- Paper ID
- 12897
- Venue
- VLDB
- Year
- 2022
- Pagerank
- 4.5297787e-05
- Overall Rank
- 8,388 | 41.65%
- DOI
-
10.14778/3565838.3565841
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 11 of 11 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
| 7,172 |
Summarized Causal Explanations For Aggregate Views |
2024 |
SIGMOD |
4.8114797e-05 |
| 8,862 |
TabEE: Tabular Embeddings Explanations |
2024 |
SIGMOD |
4.4331977e-05 |
| 9,644 |
Fair and Actionable Causal Prescription Ruleset |
2025 |
SIGMOD |
4.3109001e-05 |
| 10,015 |
Differentially Private Explanations for Clusters |
2026 |
SIGMOD |
4.1945683e-05 |
| 10,147 |
Causal Explanations for Disparate Trends: Where and Why? |
2026 |
SIGMOD |
4.1945683e-05 |
| 10,740 |
Finding Convincing Views to Endorse a Claim |
2025 |
VLDB |
4.1945683e-05 |
| 10,784 |
Towards Automated Cross-domain Exploratory Data Analysis through Large Language Models |
2025 |
VLDB |
4.1945683e-05 |
| 10,875 |
SDEcho: Efficient Explanation of Aggregated Sequence Difference |
2025 |
VLDB |
4.1945683e-05 |
| 11,123 |
PD-Explain: A Unified Python-native Framework for Query Explanations Over DataFrames |
2024 |
VLDB |
4.1945683e-05 |
| 13,099 |
Demonstration of DPClustX: Differentially Private Explanations for Clusters |
2025 |
SIGMOD |
- |
| 13,163 |
Demonstrating TabEE: Tabular Embedding Explanations |
2024 |
VLDB |
- |
Outgoing Citations (Sorted by Pagerank)
Showing 24 of 24 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 31 |
Provenance Semirings |
2007 |
PODS |
0.0007857786 |
| 214 |
Scorpion: Explaining Away Outliers in Aggregate Queries |
2013 |
VLDB |
0.0003363692 |
| 224 |
CORDS: Automatic Discovery of Correlations and Soft Functional Dependencies |
2004 |
SIGMOD |
0.00032746205 |
| 460 |
SeeDB: Efficient Data-Driven Visualization Recommendations to Support Visual Analytics |
2015 |
VLDB |
0.00022516069 |
| 487 |
Why Not? |
2009 |
SIGMOD |
0.00022050218 |
| 942 |
A Formal Approach to Finding Explanations for Database Queries |
2014 |
SIGMOD |
0.00015155714 |
| 1,009 |
SnipSuggest: Context-Aware Autocompletion for SQL |
2011 |
VLDB |
0.00014653644 |
| 1,106 |
Provenance for Aggregate Queries |
2011 |
PODS |
0.0001398766 |
| 1,993 |
Automatically Generating Data Exploration Sessions Using Deep Reinforcement Learning |
2020 |
SIGMOD |
9.8453334e-05 |
| 3,252 |
Auto-Suggest: Learning-to-Recommend Data Preparation Steps Using Data Science Notebooks |
2020 |
SIGMOD |
7.3178277e-05 |
| 3,546 |
Extracting Top-K Insights from Multi-dimensional Data |
2017 |
SIGMOD |
6.9870745e-05 |
| 3,686 |
On Detecting Cherry-picked Trendlines |
2020 |
VLDB |
6.84423e-05 |
| 4,331 |
Exploratory Keyword Search with Interactive Input |
2015 |
SIGMOD |
6.2836568e-05 |
| 4,564 |
Learning User Preferences By Adaptive Pairwise Comparison |
2015 |
VLDB |
6.0819005e-05 |
| 4,851 |
Provenance for Natural Language Queries |
2017 |
VLDB |
5.8768322e-05 |
| 5,191 |
Going Beyond Provenance: Explaining Query Answers with Pattern-based Counterbalances |
2019 |
SIGMOD |
5.6378768e-05 |
| 5,217 |
QuickInsights: Quick and Automatic Discovery of Insights from Multi-Dimensional Data |
2019 |
SIGMOD |
5.6227959e-05 |
| 5,418 |
High-Level Why-Not Explanations using Ontologies |
2015 |
PODS |
5.5178123e-05 |
| 5,472 |
Guided Exploration of User Groups |
2020 |
VLDB |
5.4888146e-05 |
| 5,691 |
Putting Things into Context: Rich Explanations for Query Answers using Join Graphs |
2021 |
SIGMOD |
5.3684557e-05 |
| 6,744 |
MapRat: Meaningful Explanation, Interactive Exploration and Geo-Visualization of Collaborative Ratings |
2012 |
VLDB |
4.9419773e-05 |
| 7,565 |
ExRank: An Exploratory Ranking Interface |
2016 |
VLDB |
4.7091711e-05 |
| 9,697 |
PCOR: Private Contextual Outlier Release via Differentially Private Search |
2021 |
SIGMOD |
4.3022295e-05 |
| 9,698 |
ExTuNe: Explaining Tuple Non-conformance |
2020 |
SIGMOD |
4.3022295e-05 |
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