Interactive Data Harmonization with LLM Agents: Opportunities and Challenges
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866911095019536384 |
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| author | Santos, Aécio Pena, Eduardo H. M. Lopez, Roque Freire, Juliana |
| author_facet | Santos, Aécio Pena, Eduardo H. M. Lopez, Roque Freire, Juliana |
| contents | Data harmonization is an essential task that entails integrating datasets from diverse sources. Despite years of research in this area, it remains a time-consuming and challenging task due to schema mismatches, varying terminologies, and differences in data collection methodologies. This paper presents the case for agentic data harmonization as a means to both empower experts to harmonize their data and to streamline the process. We introduce Harmonia, a system that combines LLM-based reasoning, an interactive user interface, and a library of data harmonization primitives to automate the synthesis of data harmonization pipelines. We demonstrate Harmonia in a clinical data harmonization scenario, where it helps to interactively create reusable pipelines that map datasets to a standard format. Finally, we discuss challenges and open problems, and suggest research directions for advancing our vision. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_07132 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Interactive Data Harmonization with LLM Agents: Opportunities and Challenges Santos, Aécio Pena, Eduardo H. M. Lopez, Roque Freire, Juliana Artificial Intelligence Databases Data harmonization is an essential task that entails integrating datasets from diverse sources. Despite years of research in this area, it remains a time-consuming and challenging task due to schema mismatches, varying terminologies, and differences in data collection methodologies. This paper presents the case for agentic data harmonization as a means to both empower experts to harmonize their data and to streamline the process. We introduce Harmonia, a system that combines LLM-based reasoning, an interactive user interface, and a library of data harmonization primitives to automate the synthesis of data harmonization pipelines. We demonstrate Harmonia in a clinical data harmonization scenario, where it helps to interactively create reusable pipelines that map datasets to a standard format. Finally, we discuss challenges and open problems, and suggest research directions for advancing our vision. |
| title | Interactive Data Harmonization with LLM Agents: Opportunities and Challenges |
| topic | Artificial Intelligence Databases |
| url | https://arxiv.org/abs/2502.07132 |