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CADENZA in Action: Breaking the Monolith with Intent-Dependent Plan Spaces for Semantic Queries

Summary: CADENZA compiles natural-language intents into decomposed, intent-dependent plan spaces, replacing monolithic LLM/embedding optimization. It selects and tunes physical implementations under user quality–latency–cost preferences, exposed via an interactive multimodal-data demo. (summarized by gpt-5.6-luna on Aug 28 2026)

Paper ID
hbd79fc6b2dc06a2c
Venue
VLDB
Year
2026
Pagerank
4.9793485e-05
Overall Rank
10,978 | 26.20%
DOI
10.14778/3827998.3828084

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BibTeX Citation

@article{ha_vldb26,
        title = {{CADENZA in Action: Breaking the Monolith with Intent-Dependent Plan Spaces for Semantic Queries}},
        author = {Ha, Jaehyun and Park, Yongjoo and Han, Wook-Shin},
        journal = {PVLDB},
        series = {{VLDB} '26},
        volume = {19},
        number = {12},
        pages = {4634--4637},
        doi = {10.14778/3827998.3828084},
        url = {https://doi.org/10.14778/3827998.3828084},
        year = {2026}
}

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