SHEVA: A Visual Analytics System for Statistical Hypothesis Exploration
Summary: SHEVA combines visual EDA with multiple-hypothesis control, adjusting significance for coverage and novelty to yield representative insights. It interactively recommends hierarchical one-/two-sample tests, supports user overrides, interpretable mappings, and reusable hypothesis pipelines. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Vicente Nejar de Almeida (Federal University of Rio Grande do Sul)
- 2. Eduardo Ribeiro (Federal University of Tocantins)
- 3. Nassim Bouarour (University of Grenoble)
- 4. João Luiz Dihl Comba (Federal University of Rio Grande do Sul)
- 5. Sihem Amer-Yahia (National Centre for Scientific Research; University of Grenoble)
BibTeX Citation
@article{almeida_vldb23,
title = {{SHEVA: A Visual Analytics System for Statistical Hypothesis Exploration}},
author = {de Almeida, Vicente Nejar and Ribeiro, Eduardo and Bouarour, Nassim and Comba, João Luiz Dihl and Amer-Yahia, Sihem},
journal = {PVLDB},
series = {{VLDB} '23},
volume = {16},
number = {12},
pages = {4102--4105},
doi = {10.14778/3611540.3611631},
url = {https://doi.org/10.14778/3611540.3611631},
year = {2023}
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,258 | Controlling False Discoveries During Interactive Data Exploration | 2017 | SIGMOD | 7.5897862e-05 |
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