Efficient Approximation Framework for Attribute Recommendation
Summary: General approximation framework for top-k attribute recommendation in OLAP trend analysis; supports diverse metrics beyond fixed pairs (e.g., KS-test, Chebyshev, EMD, Euclidean). Bounds-based estimation with theoretical guarantees enables fast approximate top-k queries, yielding up to 10x speedups and high accuracy on four real datasets vs TopKAttr. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Xingguang Chen (Chinese University of Hong Kong)
- 2. Fangyuan Zhang (Chinese University of Hong Kong)
- 3. Jinchao Huang (Chinese University of Hong Kong)
- 4. Sibo Wang (Chinese University of Hong Kong)
BibTeX Citation
@inproceedings{chen_sigmod23,
title = {{Efficient Approximation Framework for Attribute Recommendation}},
author = {Chen, Xingguang and Zhang, Fangyuan and Huang, Jinchao and Wang, Sibo},
series = {{SIGMOD} '23},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3626726},
url = {https://dl.acm.org/doi/10.1145/3626726},
year = {2023}
}
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| 10,248 | Generalized Entity Matching with Adaptivity via Large Language Models | 2026 | SIGMOD | 5.093636e-05 |
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