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: h29edaa106171273b
Venue: VLDB
Year: 2022
Pagerank: 5.3456868e-05
Overall Rank: 8,360 | 43.82%
DOI: 10.14778/3565838.3565841
PDF: Download (CC BY-NC-ND 4.0)

Incoming Non-self Citations Over Time

Authors

1. Daniel Deutch (Tel Aviv University)
2. Amir Gilad (Duke University)
3. Tova Milo (Tel Aviv University)
4. Amit Mualem (Tel Aviv University)
5. Amit Somech (Bar-Ilan University)

BibTeX Citation

@article{deutch_vldb22,
        title = {{FEDEX: An Explainability Framework for Data Exploration Steps}},
        author = {Deutch, Daniel and Gilad, Amir and Milo, Tova and Mualem, Amit and Somech, Amit},
        journal = {PVLDB},
        series = {{VLDB} '22},
        volume = {15},
        number = {13},
        pages = {3854--3868},
        doi = {10.14778/3565838.3565841},
        url = {https://doi.org/10.14778/3565838.3565841},
        year = {2022}
}

Incoming Citations (Sorted by Pagerank)

Showing 11 of 11 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
6,438	Summarized Causal Explanations For Aggregate Views	2024	SIGMOD	5.7826256e-05
9,054	TabEE: Tabular Embeddings Explanations	2024	SIGMOD	5.2287012e-05
9,957	Fair and Actionable Causal Prescription Ruleset	2025	SIGMOD	5.1014161e-05
10,534	Differentially Private Explanations for Clusters	2026	SIGMOD	4.9769913e-05
10,636	Causal Explanations for Disparate Trends: Where and Why?	2026	SIGMOD	4.9769913e-05
11,369	Finding Convincing Views to Endorse a Claim	2025	VLDB	4.9769913e-05
11,385	Towards Automated Cross-domain Exploratory Data Analysis through Large Language Models	2025	VLDB	4.9769913e-05
11,456	SDEcho: Efficient Explanation of Aggregated Sequence Difference	2025	VLDB	4.9769913e-05
11,653	PD-Explain: A Unified Python-native Framework for Query Explanations Over DataFrames	2024	VLDB	4.9769913e-05
13,632	Demonstration of DPClustX: Differentially Private Explanations for Clusters	2025	SIGMOD	-
13,688	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
17	Provenance Semirings	2007	PODS	0.00059813669
160	CORDS: Automatic Discovery of Correlations and Soft Functional Dependencies	2004	SIGMOD	0.00027827605
190	Scorpion: Explaining Away Outliers in Aggregate Queries	2013	VLDB	0.0002582857
389	Why Not?	2009	SIGMOD	0.00019313101
395	SeeDB: Efficient Data-Driven Visualization Recommendations to Support Visual Analytics	2015	VLDB	0.00019156481
671	A Formal Approach to Finding Explanations for Database Queries	2014	SIGMOD	0.00014948069
811	Provenance for Aggregate Queries	2011	PODS	0.00013746145
862	SnipSuggest: Context-Aware Autocompletion for SQL	2011	VLDB	0.0001339689
2,050	Automatically Generating Data Exploration Sessions Using Deep Reinforcement Learning	2020	SIGMOD	9.1186729e-05
2,642	Auto-Suggest: Learning-to-Recommend Data Preparation Steps Using Data Science Notebooks	2020	SIGMOD	8.1751637e-05
3,189	Extracting Top-K Insights from Multi-dimensional Data	2017	SIGMOD	7.5499689e-05
4,083	Learning User Preferences By Adaptive Pairwise Comparison	2015	VLDB	6.8143727e-05
4,308	Exploratory Keyword Search with Interactive Input	2015	SIGMOD	6.6720785e-05
4,684	Provenance for Natural Language Queries	2017	VLDB	6.4692258e-05
4,726	QuickInsights: Quick and Automatic Discovery of Insights from Multi-Dimensional Data	2019	SIGMOD	6.4473808e-05
4,741	Going Beyond Provenance: Explaining Query Answers with Pattern-based Counterbalances	2019	SIGMOD	6.4411456e-05
4,842	High-Level Why-Not Explanations using Ontologies	2015	PODS	6.3828247e-05
4,861	On Detecting Cherry-picked Trendlines	2020	VLDB	6.3762488e-05
5,533	Putting Things into Context: Rich Explanations for Query Answers using Join Graphs	2021	SIGMOD	6.0906707e-05
6,165	Guided Exploration of User Groups	2020	VLDB	5.8617079e-05
6,535	MapRat: Meaningful Explanation, Interactive Exploration and Geo-Visualization of Collaborative Ratings	2012	VLDB	5.7515229e-05
7,545	ExRank: An Exploratory Ranking Interface	2016	VLDB	5.4967698e-05
10,022	PCOR: Private Contextual Outlier Release via Differentially Private Search	2021	SIGMOD	5.0932019e-05
10,023	ExTuNe: Explaining Tuple Non-conformance	2020	SIGMOD	5.0932019e-05

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