TY - CONF
T1 - A model for fine-grained data citation
AU - Davidson, Susan
AU - Deutch, Daniel
AU - Milo, Tova
AU - Silvello, Gianmaria
N1 - Funding Information:
Acknowledgments The authors would like to thank Peter Buneman and Val Tannen for many fruitful discussions. Peter Buneman initially formulated several of these ideas in the context of XML. This work has been partially funded by NSF IIS 1302212, NSF ACI 1547360, and NIH 3-U01-EB-020954-02S1; by the ERC under the FP7, ERC grant MoDaS, agreement 291071; by the ISF (1636/13); and by a grant from the Blavatnik Interdisciplinary Cyber Research Center.
PY - 2017
Y1 - 2017
N2 - An increasing amount of information is being collected in structured, evolving, curated databases, driving the question of how information extracted from such datasets via queries should be cited. Unlike traditional research products which have a fixed granularity, data citation is a challenge because the granularity varies. Different portions of the database, with varying granularity, may have different citations. Furthermore, there are an infinite number of queries over a database, so we cannot hope to explicitly attach a citation to every possible result set and/or query. We present the novel problem of automatically generating citations for general queries over a relational database, and explore a solution based on citation views, each of which attaches a citation to a view of the database. Citation views are then used to automatically construct citations for general queries.
AB - An increasing amount of information is being collected in structured, evolving, curated databases, driving the question of how information extracted from such datasets via queries should be cited. Unlike traditional research products which have a fixed granularity, data citation is a challenge because the granularity varies. Different portions of the database, with varying granularity, may have different citations. Furthermore, there are an infinite number of queries over a database, so we cannot hope to explicitly attach a citation to every possible result set and/or query. We present the novel problem of automatically generating citations for general queries over a relational database, and explore a solution based on citation views, each of which attaches a citation to a view of the database. Citation views are then used to automatically construct citations for general queries.
UR - http://www.scopus.com/inward/record.url?scp=85035015287&partnerID=8YFLogxK
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AN - SCOPUS:85035015287
SP - 17
EP - 24
T2 - 25th Italian Symposium on Advanced Database Systems, SEBD 2017
Y2 - 25 June 2017 through 29 June 2017
ER -