ExplainED: Explanations for EDA Notebooks
Summary: ExplainED automatically annotates undocumented EDA notebook views with textual explanations. It combines multi-facet interestingness scoring with Shapley-value attribution to identify and explain the views’ salient elements. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Daniel Deutch (Tel Aviv University)
- 2. Amir Gilad (Tel Aviv University)
- 3. Tova Milo (Tel Aviv University)
- 4. Amit Somech (Tel Aviv University)
BibTeX Citation
@article{deutch_vldb20,
title = {{ExplainED: Explanations for EDA Notebooks}},
author = {Deutch, Daniel and Gilad, Amir and Milo, Tova and Somech, Amit},
journal = {PVLDB},
series = {{VLDB} '20},
volume = {13},
number = {12},
pages = {2917--2920},
doi = {10.14778/3415478.3415508},
url = {https://doi.org/10.14778/3415478.3415508},
year = {2020}
}Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,640 | DataPrep.EDA: Task-Centric Exploratory Data Analysis for Statistical Modeling in Python | 2021 | SIGMOD | 6.0548401e-05 |
| 8,150 | P-Shapley: Shapley Values on Probabilistic Classifiers | 2024 | VLDB | 5.39006e-05 |
| 11,119 | SHARQ: Explainability Framework for Association Rules on Relational Data | 2025 | SIGMOD | 4.9793485e-05 |
| 11,647 | PD-Explain: A Unified Python-native Framework for Query Explanations Over DataFrames | 2024 | VLDB | 4.9793485e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 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 | 0.00059752575 |
| 189 | Scorpion: Explaining Away Outliers in Aggregate Queries | 2013 | VLDB | 0.00025840026 |
| 670 | A Formal Approach to Finding Explanations for Database Queries | 2014 | SIGMOD | 0.00014954494 |
| 2,048 | Automatically Generating Data Exploration Sessions Using Deep Reinforcement Learning | 2020 | SIGMOD | 9.1229916e-05 |
| 4,687 | Provenance for Natural Language Queries | 2017 | VLDB | 6.4706848e-05 |
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