{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,2,9]],"date-time":"2024-02-09T23:30:24Z","timestamp":1707521424131},"reference-count":8,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2018,8]]},"abstract":"<jats:p>\n            Explaining why an answer is (not) in the result of a query has proven to be of immense importance for many applications. However, why-not provenance, and to a lesser degree also why-provenance, can be very large, even for small input datasets. The resulting scalability and usability issues have limited the applicability of provenance. We present\n            <jats:italic>PUG<\/jats:italic>\n            , a system for why and why-not provenance that applies a range of novel techniques to overcome these challenges. Specifically, PUG limits provenance capture to what is relevant to explain a (missing) result of interest and uses an efficient sampling-based\n            <jats:italic>summarization<\/jats:italic>\n            method to produce compact explanations for (missing) answers. Using two real-world datasets, we demonstrate how a user can draw meaningful insights from explanations produced by PUG.\n          <\/jats:p>","DOI":"10.14778\/3229863.3236233","type":"journal-article","created":{"date-parts":[[2018,9,10]],"date-time":"2018-09-10T12:12:28Z","timestamp":1536581548000},"page":"1954-1957","source":"Crossref","is-referenced-by-count":7,"title":["Provenance summaries for answers and non-answers"],"prefix":"10.14778","volume":"11","author":[{"given":"Seokki","family":"Lee","sequence":"first","affiliation":[{"name":"Illinois Institute of Technology"}]},{"given":"Bertram","family":"Lud\u00e4scher","sequence":"additional","affiliation":[{"name":"University of Illinois"}]},{"given":"Boris","family":"Glavic","sequence":"additional","affiliation":[{"name":"Illinois Institute of Technology"}]}],"member":"320","published-online":{"date-parts":[[2018,8]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.14778\/3055540.3055550"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.14778\/2824032.2824039"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.14778\/2735461.2735467"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1265530.1265535"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.14778\/1920841.1920869"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2017.105"},{"key":"e_1_2_1_7_1","volume-title":"TaPP","author":"Lee S.","year":"2017","unstructured":"S. Lee , X. Niu , B. Lud\u00e4scher , and B. Glavic . Integrating approximate summarization with provenance capture . In TaPP , 2017 . S. Lee, X. Niu, B. Lud\u00e4scher, and B. Glavic. Integrating approximate summarization with provenance capture. In TaPP, 2017."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.14778\/1880172.1880176"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3229863.3236233","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T10:09:02Z","timestamp":1672222142000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3229863.3236233"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,8]]},"references-count":8,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2018,8]]}},"alternative-id":["10.14778\/3229863.3236233"],"URL":"https:\/\/doi.org\/10.14778\/3229863.3236233","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2018,8]]}}}