{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,5]],"date-time":"2025-10-05T16:59:17Z","timestamp":1759683557477},"reference-count":41,"publisher":"Association for Computing Machinery (ACM)","issue":"7","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2019,3]]},"abstract":"<jats:p>\n            As research products expand to include structured datasets, the challenge arises of how to automatically generate citations to the results of arbitrary queries against such datasets. Previous work explored this problem in the context of\n            <jats:italic>conjunctive<\/jats:italic>\n            queries and views using a Rewriting-Based Model (RBM). However, an increasing number of scientific queries are\n            <jats:italic>aggregate,<\/jats:italic>\n            e.g. statistical summaries of the underlying data, for which the RBM cannot be easily extended. In this paper, we show how a Provenance-Based Model (PBM) can be leveraged to 1) generate citations to conjunctive as well as aggregate queries and views; 2) associate citations with individual result tuples to enable arbitrary subsets of the result set to be cited (\n            <jats:italic>fine-grained citations<\/jats:italic>\n            ); and 3) be optimized to return citations in\n            <jats:italic>acceptable time.<\/jats:italic>\n            Our implementation of PBM in ProvCite shows that it not only handles a larger class of queries and views than RBM, but can outperform it when restricted to conjunctive views in some cases.\n          <\/jats:p>","DOI":"10.14778\/3317315.3317317","type":"journal-article","created":{"date-parts":[[2019,5,1]],"date-time":"2019-05-01T13:27:58Z","timestamp":1556717278000},"page":"738-751","source":"Crossref","is-referenced-by-count":5,"title":["ProvCite"],"prefix":"10.14778","volume":"12","author":[{"given":"Yinjun","family":"Wu","sequence":"first","affiliation":[{"name":"University of Pennsylvania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdussalam","family":"Alawini","sequence":"additional","affiliation":[{"name":"University of Illinois at Urbana-Champaign"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel","family":"Deutch","sequence":"additional","affiliation":[{"name":"Tel Aviv University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tova","family":"Milo","sequence":"additional","affiliation":[{"name":"Tel Aviv University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Susan","family":"Davidson","sequence":"additional","affiliation":[{"name":"University of Pennsylvania"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2019,3]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Out of Mind: The Current State of Practice, Policy, and Technology for the Citation of Data","author":"Cite Out","year":"2013","unstructured":"Out of Cite , Out of Mind: The Current State of Practice, Policy, and Technology for the Citation of Data , volume 12 . CODATA-ICSTI Task Group on Data Citation Standards and Practices, 2013 . Out of Cite, Out of Mind: The Current State of Practice, Policy, and Technology for the Citation of Data, volume 12. CODATA-ICSTI Task Group on Data Citation Standards and Practices, 2013."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcss.2006.10.019"},{"key":"e_1_2_1_4_1","unstructured":"A. Alawini S. Davidson S. Pandey G. Silvello and Y. Wu. \"DBLP-NSF dataset SQL dump\" Mendeley Data v5.  A. Alawini S. Davidson S. Pandey G. Silvello and Y. Wu. \"DBLP-NSF dataset SQL dump\" Mendeley Data v5."},{"key":"e_1_2_1_5_1","volume-title":"Data citation: A new provenance challenge. Data Engineering, page 27","author":"Alawini A.","year":"2018","unstructured":"A. Alawini , S. Davidson , G. Silvello , V. Tannen , and Y. Wu . Data citation: A new provenance challenge. Data Engineering, page 27 , 2018 . A. Alawini, S. Davidson, G. Silvello, V. Tannen, and Y. Wu. Data citation: A new provenance challenge. Data Engineering, page 27, 2018."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.14778\/3137765.3137799"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/2389241.2389249"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/1989284.1989302"},{"key":"e_1_2_1_9_1","volume-title":"GProM-a swiss army knife for your provenance needs. Data Eng. Bull, 41(1):51--62","author":"Arab B. S.","year":"2018","unstructured":"B. S. Arab , S. Feng , B. Glavic , S. Lee , X. Niu , and Q. Zeng . GProM-a swiss army knife for your provenance needs. Data Eng. Bull, 41(1):51--62 , 2018 . B. S. Arab, S. Feng, B. Glavic, S. Lee, X. Niu, and Q. Zeng. GProM-a swiss army knife for your provenance needs. Data Eng. 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