ONION: A Multi-Layered Framework for Participatory ER Design
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908446720262144 |
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| author | Makovska, Viktoriia Fletcher, George Stoyanovich, Julia |
| author_facet | Makovska, Viktoriia Fletcher, George Stoyanovich, Julia |
| contents | We present ONION, a multi-layered framework for participatory Entity-Relationship (ER) modeling that integrates insights from design justice, participatory AI, and conceptual modeling. ONION introduces a five-stage methodology: Observe, Nurture, Integrate, Optimize, Normalize. It supports progressive abstraction from unstructured stakeholder input to structured ER diagrams.
Our approach aims to reduce designer bias, promote inclusive participation, and increase transparency through the modeling process. We evaluate ONION through real-world workshops focused on sociotechnical systems in Ukraine, highlighting how diverse stakeholder engagement leads to richer data models and deeper mutual understanding. Early results demonstrate ONION's potential to host diversity in early-stage data modeling. We conclude with lessons learned, limitations and challenges involved in scaling and refining the framework for broader adoption. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_08702 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ONION: A Multi-Layered Framework for Participatory ER Design Makovska, Viktoriia Fletcher, George Stoyanovich, Julia Databases Artificial Intelligence Computers and Society We present ONION, a multi-layered framework for participatory Entity-Relationship (ER) modeling that integrates insights from design justice, participatory AI, and conceptual modeling. ONION introduces a five-stage methodology: Observe, Nurture, Integrate, Optimize, Normalize. It supports progressive abstraction from unstructured stakeholder input to structured ER diagrams. Our approach aims to reduce designer bias, promote inclusive participation, and increase transparency through the modeling process. We evaluate ONION through real-world workshops focused on sociotechnical systems in Ukraine, highlighting how diverse stakeholder engagement leads to richer data models and deeper mutual understanding. Early results demonstrate ONION's potential to host diversity in early-stage data modeling. We conclude with lessons learned, limitations and challenges involved in scaling and refining the framework for broader adoption. |
| title | ONION: A Multi-Layered Framework for Participatory ER Design |
| topic | Databases Artificial Intelligence Computers and Society |
| url | https://arxiv.org/abs/2507.08702 |