@inproceedings{7a2887fff0c743a3b73d8a4d80d2bf20,
title = "Data citation: Giving credit where credit is due",
abstract = "An increasing amount of information is being published in structured databases and retrieved using queries, raising the question of how query results should be cited. Since there are a large number of possible queries over a database, one strategy is to specify citations to a small set of frequent queries-citation views-and use these to construct citations to other {"}general{"} queries. We present three approaches to implementing citation views and describe alternative policies for the joint, alternate and aggregated use of citation views. Extensive experiments using both synthetic and realistic citation views and queries show the tradeoffs between the approaches in terms of the time to generate citations, as well as the size of the resulting citation. They also show that the choice of policy has a huge effect both on performance and size, leading to useful guidelines for what policies to use and how to specify citation views.",
keywords = "Data citation, Provenance, Scientific databases",
author = "Yinjun Wu and Abdussalam Alawini and Davidson, {Susan B.} and Gianmaria Silvello",
note = "Publisher Copyright: {\textcopyright} 2018 Association for Computing Machinery.; 44th ACM SIGMOD International Conference on Management of Data, SIGMOD 2018 ; Conference date: 10-06-2018 Through 15-06-2018",
year = "2018",
month = may,
day = "27",
doi = "10.1145/3183713.3196910",
language = "English (US)",
series = "Proceedings of the ACM SIGMOD International Conference on Management of Data",
publisher = "Association for Computing Machinery",
pages = "99--114",
editor = "Gautam Das and Christopher Jermaine and Ahmed Eldawy and Philip Bernstein",
booktitle = "SIGMOD 2018 - Proceedings of the 2018 International Conference on Management of Data",
address = "United States",
}