{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,5]],"date-time":"2025-10-05T16:50:04Z","timestamp":1759683004874},"reference-count":9,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>\n            In this demonstration, we will present LensXPlain, an interactive system to help users understand answers of aggregate queries by providing meaningful explanations. Given a SQL group-by query and a question from a user \"\n            <jats:italic>why output o is high\/low<\/jats:italic>\n            \", or \"\n            <jats:italic>why output o<\/jats:italic>\n            <jats:sub>1<\/jats:sub>\n            <jats:italic>is higher\/lower than o<\/jats:italic>\n            <jats:sub>2<\/jats:sub>\n            \", LensXPlain helps users explore the results and find subsets of tuples captured by predicates that contributed the most toward such observations. The contributions are measured either by\n            <jats:italic>intervention<\/jats:italic>\n            (if the contributing tuples are removed, the values or the ratios in the user question change in the opposite direction), or by\n            <jats:italic>aggravation<\/jats:italic>\n            (if the query is restricted to the contributing tuples, the observations change more in the same direction). LensXPlain uses ensemble learning for recommending useful attributes in explanations, and employs a suite of optimizations to enable explanation generation and refinement at an interactive speed. In the demonstration, the audience can run aggregation queries over real world datasets, browse the answers using a graphical user interface, ask questions on unexpected\/interesting query results with simple visualizations, and explore and refine explanations returned by LensXPlain.\n          <\/jats:p>","DOI":"10.14778\/3352063.3352094","type":"journal-article","created":{"date-parts":[[2019,9,18]],"date-time":"2019-09-18T18:36:11Z","timestamp":1568831771000},"page":"1898-1901","source":"Crossref","is-referenced-by-count":7,"title":["LensXPlain"],"prefix":"10.14778","volume":"12","author":[{"given":"Zhengjie","family":"Miao","sequence":"first","affiliation":[{"name":"Duke University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrew","family":"Lee","sequence":"additional","affiliation":[{"name":"Duke University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sudeepa","family":"Roy","sequence":"additional","affiliation":[{"name":"Duke University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2019,8]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"http:\/\/www.tableausoftware.com\/.  http:\/\/www.tableausoftware.com\/."},{"key":"e_1_2_1_2_1","first-page":"487","volume-title":"VLDB","author":"Agrawal R.","year":"1994"},{"key":"e_1_2_1_3_1","volume-title":"Cambridge University Press","author":"Pearl J.","year":"2000"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2588555.2588578"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2016.2599030"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.14778\/3025111.3025126"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.14778\/2831360.2831371"},{"key":"e_1_2_1_8_1","doi-asserted-by":"crossref","unstructured":"K. Wongsuphasawat D. Moritz A. Anand J. Mackinlay B. Howe and J. Heer. Voyager: Exploratory analysis via faceted browsing of visualization recommendations. IEEE transactions on visualization and computer graphics 22(1):649--658 2016.  K. Wongsuphasawat D. Moritz A. Anand J. Mackinlay B. Howe and J. Heer. Voyager: Exploratory analysis via faceted browsing of visualization recommendations. IEEE transactions on visualization and computer graphics 22(1):649--658 2016.","DOI":"10.1109\/TVCG.2015.2467191"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.14778\/2536354.2536356"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3352063.3352094","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T10:32:41Z","timestamp":1672223561000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3352063.3352094"}},"subtitle":["visualizing and explaining contributing subsets for aggregate query answers"],"short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":9,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2019,8]]}},"alternative-id":["10.14778\/3352063.3352094"],"URL":"https:\/\/doi.org\/10.14778\/3352063.3352094","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2019,8]]}}}