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Mondrian: Spreadsheet Layout Detection

Summary: Automates multiregion spreadsheet layout detection by identifying multiple regions in a single file and extracting recurring templates across files. Graph-based layouts enable similarity-based template discovery; a web UI supports automated preparation. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6430
Venue
SIGMOD
Year
2022
Pagerank
5.6841406e-05
Overall Rank
7,167 | 50.83%
DOI
10.1145/3514221.3520152

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{vitagliano_sigmod22,
        title = {{Mondrian: Spreadsheet Layout Detection}},
        author = {Vitagliano, Gerardo and Reisener, Lucas and Jiang, Lan and Hameed, Mazhar and Naumann, Felix},
        series = {{SIGMOD} '22},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3514221.3520152},
        url = {https://dl.acm.org/doi/10.1145/3514221.3520152},
        year = {2022}
}

Incoming Citations (Sorted by Pagerank)

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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
4,054 Enterprise Information Extraction: Recent Developments and Open Challenges 2010 SIGMOD 6.9352304e-05
4,168 Pytheas: Pattern-based Table Discovery in CSV Files 2020 VLDB 6.8570481e-05
11,618 Detecting Layout Templates in Complex Multiregion Files 2022 VLDB 5.093636e-05
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