KathDB: Explainable Multimodal Database Management System with Human-AI Collaboration
Summary: KathDB embeds foundation-model reasoning into a relational DBMS to support multimodal queries (tables, text, images, video) while preserving relational semantics and optimization. Introduces human-AI interaction at parsing, execution, and explanation for iterative, explainable results avoiding black-box LLM or manual ML-UDF workflows. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Guorui Xiao (University of Washington)
- 2. Enhao Zhang (University of Washington)
- 3. Nicole Sullivan (University of Washington)
- 4. Will Hansen (University of Washington)
- 5. Magdalena Balazinska (University of Washington)
BibTeX Citation
@inproceedings{xiao_cidr26,
address = {Amsterdam, Netherlands},
series = {{CIDR} '26},
title = {{KathDB: Explainable Multimodal Database Management System with Human-AI Collaboration}},
booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
author = {Xiao, Guorui and Zhang, Enhao and Sullivan, Nicole and Hansen, Will and Balazinska, Magdalena},
year = {2026}
}
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