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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
13085
Venue
VLDB
Year
2022
Pagerank
5.4709725e-05
Overall Rank
8,184 | 43.86%
DOI
10.14778/3565838.3565841

Incoming Non-self Citations Over Time

Authors

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.

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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 0.00059843817
159 CORDS: Automatic Discovery of Correlations and Soft Functional Dependencies 2004 SIGMOD 0.00028129426
191 Scorpion: Explaining Away Outliers in Aggregate Queries 2013 VLDB 0.00026096009
385 Why Not? 2009 SIGMOD 0.00019455743
410 SeeDB: Efficient Data-Driven Visualization Recommendations to Support Visual Analytics 2015 VLDB 0.0001890421
663 A Formal Approach to Finding Explanations for Database Queries 2014 SIGMOD 0.00015174751
806 Provenance for Aggregate Queries 2011 PODS 0.00013890398
846 SnipSuggest: Context-Aware Autocompletion for SQL 2011 VLDB 0.00013658824
2,058 Automatically Generating Data Exploration Sessions Using Deep Reinforcement Learning 2020 SIGMOD 9.2480248e-05
2,744 Auto-Suggest: Learning-to-Recommend Data Preparation Steps Using Data Science Notebooks 2020 SIGMOD 8.1781662e-05
3,132 Extracting Top-K Insights from Multi-dimensional Data 2017 SIGMOD 7.7246394e-05
3,984 Learning User Preferences By Adaptive Pairwise Comparison 2015 VLDB 6.9739437e-05
4,249 Exploratory Keyword Search with Interactive Input 2015 SIGMOD 6.8042877e-05
4,602 Provenance for Natural Language Queries 2017 VLDB 6.6107638e-05
4,628 QuickInsights: Quick and Automatic Discovery of Insights from Multi-Dimensional Data 2019 SIGMOD 6.5962102e-05
4,638 Going Beyond Provenance: Explaining Query Answers with Pattern-based Counterbalances 2019 SIGMOD 6.5919801e-05
4,746 On Detecting Cherry-picked Trendlines 2020 VLDB 6.5256876e-05
4,747 High-Level Why-Not Explanations using Ontologies 2015 PODS 6.5252099e-05
5,396 Putting Things into Context: Rich Explanations for Query Answers using Join Graphs 2021 SIGMOD 6.2329373e-05
6,039 Guided Exploration of User Groups 2020 VLDB 5.9990875e-05
6,425 MapRat: Meaningful Explanation, Interactive Exploration and Geo-Visualization of Collaborative Ratings 2012 VLDB 5.8810029e-05
7,399 ExRank: An Exploratory Ranking Interface 2016 VLDB 5.6255964e-05
9,830 PCOR: Private Contextual Outlier Release via Differentially Private Search 2021 SIGMOD 5.2125702e-05
9,831 ExTuNe: Explaining Tuple Non-conformance 2020 SIGMOD 5.2125702e-05
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