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)
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}
}
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