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Repairing Data through Regular Expressions

Summary: Introduces regex-driven repair for sequence data, separating structural edits enforcing an NFA-recognized pattern from token-value correction. RSR computes prefix-to-pattern edit distance in O(nm²), while a unified criterion selects rule- or edit-distance-based value repairs. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
11522
Venue
VLDB
Year
2016
Pagerank
5.2556545e-05
Overall Rank
9,523 | 34.67%
DOI
10.14778/2876473.2876478

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{li_vldb16,
        title = {{Repairing Data through Regular Expressions}},
        author = {Li, Zeyu and Wang, Hongzhi and Shao, Wei and Li, Jianzhong and Gao, Hong},
        journal = {PVLDB},
        series = {{VLDB} '16},
        volume = {9},
        number = {5},
        pages = {432},
        doi = {10.14778/2876473.2876478},
        url = {https://doi.org/10.14778/2876473.2876478},
        year = {2016}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
9,973 Exploiting Structure in Regular Expression Queries 2023 SIGMOD 5.1845938e-05
10,505 The Case For Language Model Approximated LIKE Predicate 2026 SIGMOD 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 7 of 7 cited papers.

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

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