TARImpute: Task-Aware auto-Recommender System for Missing Value Imputation Algorithms with Clustering Case Studies
Summary: TARImpute is a task-aware recommender that jointly evaluates missing-value imputation and clustering, rather than optimizing imputation in isolation. Its impact profiler, error-propagation interpreter, and adaptive optimizer provide explainable method selection and downstream-quality assessment. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Xiaoou Ding (Harbin Engineering University)
- 2. Yanshuo Liu (Harbin Engineering University)
- 3. Zhounan Chen (Harbin Engineering University)
- 4. Hongzhi Wang (Harbin Engineering University)
- 5. Chen Wang (Tsinghua University)
- 6. Jianmin Wang (Tsinghua University)
BibTeX Citation
@article{ding_vldb25,
title = {{TARImpute: Task-Aware auto-Recommender System for Missing Value Imputation Algorithms with Clustering Case Studies}},
author = {Ding, Xiaoou and Liu, Yanshuo and Chen, Zhounan and Wang, Hongzhi and Wang, Chen and Wang, Jianmin},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {12},
pages = {5343--5346},
doi = {10.14778/3750601.3750667},
url = {https://doi.org/10.14778/3750601.3750667},
year = {2025}
}
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,545 | Mind the Gap: An Experimental Evaluation of Imputation of Missing Values Techniques in Time Series | 2020 | VLDB | 8.4401333e-05 |
| 5,175 | Cleanits: A Data Cleaning System for Industrial Time Series | 2019 | VLDB | 6.3332964e-05 |
| 9,697 | Clean4TSDB: A Data Cleaning Tool for Time Series Databases | 2024 | VLDB | 5.2351259e-05 |
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