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IHCS: An Integrated Hybrid Cleaning System

Summary: IHCS unifies heterogeneous data-quality rules in an MLN index, jointly detecting and repairing multiple error types. Its abnormal-group processing, multi-version generation, and conflict elimination produce consistent clean data with visual monitoring. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
12077
Venue
VLDB
Year
2019
Pagerank
5.093636e-05
Overall Rank
11,877 | 18.52%
DOI
10.14778/3352063.3352088

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Authors

BibTeX Citation

@article{ge_vldb19,
        title = {{IHCS: An Integrated Hybrid Cleaning System}},
        author = {Ge, Congcong and Gao, Yunjun and Miao, Xiaoye and Chen, Lu and Jensen, Christian S. and Zhu, Ziyuan},
        journal = {PVLDB},
        series = {{VLDB} '19},
        volume = {12},
        number = {12},
        pages = {1874--1877},
        doi = {10.14778/3352063.3352088},
        url = {https://doi.org/10.14778/3352063.3352088},
        year = {2019}
}

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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
112 HoloClean: Holistic Data Repairs with Probabilistic Inference 2017 VLDB 0.00032801121
582 ActiveClean: Interactive Data Cleaning For Statistical Modeling 2016 VLDB 0.00016148948
647 Discovering Data Quality Rules 2008 VLDB 0.00015334666
725 NADEEF: A Commodity Data Cleaning System 2013 SIGMOD 0.00014617251
1,323 Data Cleaning: Overview and Emerging Challenges 2016 SIGMOD 0.00011152602
2,398 BigDansing: A System for Big Data Cleansing 2015 SIGMOD 8.631172e-05
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