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Identifying Insufficient Data Coverage for Ordinal Continuous-Valued Attributes

Summary: Defines coverage for ordinal/continuous attributes as regions with enough similar training data to guarantee accuracy. Presents a Voronoi-diagram-based algorithm to identify uncovered regions in low dimensions and a randomized approximation for high dimensions, validated on real datasets. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6267
Venue
SIGMOD
Year
2021
Pagerank
5.6319194e-05
Overall Rank
7,367 | 49.46%
DOI
10.1145/3448016.3457315

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Authors

BibTeX Citation

@inproceedings{asudeh_sigmod21,
        title = {{Identifying Insufficient Data Coverage for Ordinal Continuous-Valued Attributes}},
        author = {Asudeh, Abolfazl and Shahbazi, Nima and Jin, Zhongjun and Jagadish, H. V.},
        series = {{SIGMOD} '21},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3448016.3457315},
        url = {https://dl.acm.org/doi/10.1145/3448016.3457315},
        year = {2021}
}

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