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MorphingDB: A Task-Centric AI-Native DBMS for Model Management and Inference

Summary: MorphingDB embeds task-centric model management into PostgreSQL with tensor types and a two‑phase transfer-learning selector (offline transferability subspace, online feature-aware projection). Pre‑embedding/vector sharing and DAG batch pipelines with cost‑aware scheduling boost inference, improving throughput and accuracy/resource/time tradeoffs over AI‑DBMS and AutoML baselines. (summarized by gpt-5-mini on Feb 11 2026)

Paper ID
7630
Venue
SIGMOD
Year
2026
Pagerank
5.093636e-05
Overall Rank
10,418 | 28.53%
DOI
10.1145/3769844

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BibTeX Citation

@inproceedings{wu_sigmod26,
        title = {{MorphingDB: A Task-Centric AI-Native DBMS for Model Management and Inference}},
        author = {Wu, Sai and Xia, Ruichen and Yang, Dingyu and Wang, Rui and Lai, Huihang and Guan, Jiarui and Bai, Jiameng and Zhang, Dongxiang and Tang, Xiu and Xie, Zhongle and Lu, Peng and Chen, Gang},
        series = {{SIGMOD} '26},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3769844},
        url = {https://dl.acm.org/doi/10.1145/3769844},
        year = {2026}
}

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