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Data Citation: a Computational Challenge

Summary: Frames data citation as a formal computational problem that blends query-answering-using-views and provenance management. Proposes an approach leveraging these connections and highlights practical and theoretical open problems for database research. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1734
Venue
PODS
Year
2017
Pagerank
5.093636e-05
Overall Rank
11,972 | 17.87%
DOI
10.1145/3034786.3056123

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

BibTeX Citation

@inproceedings{davidson_pods17,
        address = {New York, NY, USA},
        series = {{PODS} '17},
        title = {{Data Citation: a Computational Challenge}},
        url = {https://dl.acm.org/doi/10.1145/3034786.3056123},
        doi = {10.1145/3034786.3056123},
        booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
        publisher = {Association for Computing Machinery},
        author = {Davidson, Susan B. and Buneman, Peter and Deutch, Daniel and Milo, Tova and Silvello, Gianmaria},
        year = {2017}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
10,077 ProvCite: Provenance-based Data Citation 2019 VLDB 5.1603976e-05
Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 2 of 2 cited papers.

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

Rank Cited Paper Year Venue Pagerank
17 Provenance Semirings 2007 PODS 0.00059843817
11,966 A Model for Fine-Grained Data Citation 2017 CIDR 5.093636e-05
Previous Page 1 / 1 Next

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