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DataDiff: User-Interpretable Data Transformation Summaries for Collaborative Data Analysis

Summary: DataDiff provides user-interpretable data-diff summaries for collaborative dataset versioning. It yields concise explanations of changes without dependence on the originating operations, aiding merge, conflict detection, and evolution reasoning. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5587
Venue
SIGMOD
Year
2018
Pagerank
5.318789e-05
Overall Rank
9,129 | 37.37%
DOI
10.1145/3183713.3193564

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{yilmaz_sigmod18,
        title = {{DataDiff: User-Interpretable Data Transformation Summaries for Collaborative Data Analysis}},
        author = {Yilmaz, Gunce Su and Wattanawaroon, Tana and Xu, Liqi and Nigam, Abhishek and Elmore, Aaron J. and Parameswaran, Aditya},
        series = {{SIGMOD} '18},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3183713.3193564},
        url = {https://dl.acm.org/doi/10.1145/3183713.3193564},
        year = {2018}
}

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