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Pytheas: Pattern-based Table Discovery in CSV Files

Summary: Pytheas uses pattern-based line classification and column-value coherency to discover tables in loosely structured CSVs. It achieves precision/recall above 95% (vs ~89/81), generalizes across countries, and provides a confidence measure for potential errors. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12288
Venue
VLDB
Year
2020
Pagerank
6.8570481e-05
Overall Rank
4,168 | 71.41%
DOI
10.14778/3407790.3407810

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{christodoulakis_vldb20,
        title = {{Pytheas: Pattern-based Table Discovery in CSV Files}},
        author = {Christodoulakis, Christina and Munson, Eric B. and Gabel, Moshe and Brown, Angela Demke and Miller, Renée J.},
        journal = {PVLDB},
        series = {{VLDB} '20},
        volume = {13},
        number = {11},
        pages = {2075--2089},
        doi = {10.14778/3407790.3407810},
        url = {https://doi.org/10.14778/3407790.3407810},
        year = {2020}
}

Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Rank Citing Paper Year Venue Pagerank
7,167 Mondrian: Spreadsheet Layout Detection 2022 SIGMOD 5.6841406e-05
7,803 Pollock: A Data Loading Benchmark 2023 VLDB 5.5418222e-05
8,397 A Demonstration of KGLac: A Data Discovery and Enrichment Platform for Data Science 2021 VLDB 5.4344976e-05
11,618 Detecting Layout Templates in Complex Multiregion Files 2022 VLDB 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 13 of 13 cited papers.

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

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