{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T22:54:17Z","timestamp":1757631257406,"version":"3.44.0"},"reference-count":10,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2023,8]]},"abstract":"<jats:p>\n            Data management and analysis tasks are often carried out using spreadsheet software. A popular feature in most spreadsheet platforms is the ability to define data-dependent formatting rules. These rules can express actions such as\n            <jats:italic toggle=\"yes\">\"color red all entries in a column that are negative\"<\/jats:italic>\n            or\n            <jats:italic toggle=\"yes\">\"bold all rows not containing error or failure\".<\/jats:italic>\n            Unfortunately, users who want to exercise this functionality need to manually write these conditional formatting (CF) rules. We introduce Cornet, a system that automatically learns such conditional formatting rules from user examples. Cornet takes inspiration from inductive program synthesis and combines symbolic rule enumeration, based on semi-supervised clustering and iterative decision tree learning, with a neural ranker to produce accurate conditional formatting rules. In this demonstration, we show Cornet in action as a simple add-in to Microsoft's Excel. After the user provides one or two formatted cells as examples, Cornet generates formatting rule suggestions for the user to apply to the spreadsheet.\n          <\/jats:p>","DOI":"10.14778\/3611540.3611620","type":"journal-article","created":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T11:32:37Z","timestamp":1694777557000},"page":"4058-4061","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Cornet: Learning Spreadsheet Formatting Rules by Example"],"prefix":"10.14778","volume":"16","author":[{"given":"Mukul","family":"Singh","sequence":"first","affiliation":[{"name":"Microsoft, Delhi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jos\u00e9 Cambronero","family":"Sanchez","sequence":"additional","affiliation":[{"name":"Microsoft, Redmond, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sumit","family":"Gulwani","sequence":"additional","affiliation":[{"name":"Microsoft, Redmond, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vu","family":"Le","sequence":"additional","affiliation":[{"name":"Microsoft, Redmond, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carina","family":"Negreanu","sequence":"additional","affiliation":[{"name":"Microsoft Research, Cambridge, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gust","family":"Verbruggen","sequence":"additional","affiliation":[{"name":"Microsoft, Redmond, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,8]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Top-down induction of first-order logical decision trees. Artificial intelligence 101, 1--2","author":"Blockeel Hendrik","year":"1998","unstructured":"Hendrik Blockeel and Luc De Raedt. 1998. Top-down induction of first-order logical decision trees. Artificial intelligence 101, 1--2 (1998), 285--297."},{"doi-asserted-by":"publisher","key":"e_1_2_1_2_1","DOI":"10.18653\/v1\/n19-1423"},{"doi-asserted-by":"publisher","key":"e_1_2_1_3_1","DOI":"10.24963\/ijcai.2017\/227"},{"unstructured":"Microsoft Excel. 2023. Sample Excel Wrkbooks. https:\/\/www.contextures.com\/xlsampledata01.html#wos. Last Accessed: 2023-03-24.","key":"e_1_2_1_4_1"},{"doi-asserted-by":"publisher","key":"e_1_2_1_5_1","DOI":"10.1145\/1926385.1926423"},{"doi-asserted-by":"crossref","unstructured":"Vu Le and Sumit Gulwani. 2014. FlashExtract: a framework for data extraction by examples. In Programming Language Design and Implementation. ACM 542--553.","key":"e_1_2_1_6_1","DOI":"10.1145\/2594291.2594333"},{"doi-asserted-by":"publisher","key":"e_1_2_1_7_1","DOI":"10.1007\/978-3-030-01225-0_13"},{"unstructured":"Microsoft. 2022. Excel Help Forum. techcommunity.microsoft.com\/t5\/forums\/searchpage\/tab\/message?q=conditional%20formatting. Accessed: 2022-06-30.","key":"e_1_2_1_8_1"},{"unstructured":"Joseph N. 2022. Number of Google Sheets and Excel Users Worldwide. https:\/\/askwonder.com\/research\/number-google-sheets-users-worldwide-eoskdoxav. Last Accessed: 2022-07-30.","key":"e_1_2_1_9_1"},{"unstructured":"N Natarajan D Simmons N Datha P Jain and S Gulwani. 2019. Learning Natural Programs from a Few Examples in Real-Time. In AIStats. https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2019\/01\/AIStats19.pdf","key":"e_1_2_1_10_1"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3611540.3611620","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T22:35:48Z","timestamp":1757543748000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3611540.3611620"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8]]},"references-count":10,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["10.14778\/3611540.3611620"],"URL":"https:\/\/doi.org\/10.14778\/3611540.3611620","relation":{},"ISSN":["2150-8097"],"issn-type":[{"type":"print","value":"2150-8097"}],"subject":[],"published":{"date-parts":[[2023,8]]},"assertion":[{"value":"2023-08-01","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}