{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T05:16:24Z","timestamp":1755839784534,"version":"3.32.0"},"reference-count":13,"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":[[2024,8]]},"abstract":"<jats:p>Line charts are fundamental to data analysis and exploration, offering concise visual representations of trends. However, gaining access to the underlying data used to construct these charts is often challenging. In this paper, we describe DDLC (short for Dataset discovery via line charts), an automatic dataset discovery tool that is able to not only identify datasets (from a dataset repository) that are \"relevant\" to the information depicted from a line chart provided by the users, but also empower users to refine search results based on specific visual elements extracted from the line chart. Moreover, DDLC offers multiple avenues for users to validate search outcomes: 1) Providing explanations on how a similar line chart could be generated from the identified dataset; 2) enabling comparison of line charts generated from different datasets via different ways (e.g., the aggregation vs. non-aggregation operator); 3) facilitating fine-grained examination of the correspondence between the line chart and the identified dataset. By seamlessly combining dataset retrieval with visual refinement and validation mechanisms, DDLC offers a comprehensive solution for the data-driven exploration and analysis.<\/jats:p>","DOI":"10.14778\/3685800.3685857","type":"journal-article","created":{"date-parts":[[2024,11,8]],"date-time":"2024-11-08T17:25:21Z","timestamp":1731086721000},"page":"4289-4292","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Navigating Data Repositories: Utilizing Line Charts to Discover Relevant Datasets"],"prefix":"10.14778","volume":"17","author":[{"given":"Daomin","family":"Ji","sequence":"first","affiliation":[{"name":"RMIT University"}]},{"given":"Hui","family":"Luo","sequence":"additional","affiliation":[{"name":"University of Wollongong"}]},{"given":"Zhifeng","family":"Bao","sequence":"additional","affiliation":[{"name":"RMIT University"}]},{"given":"Shane","family":"Culpepper","sequence":"additional","affiliation":[{"name":"The University of Queensland"}]}],"member":"320","published-online":{"date-parts":[[2024,11,8]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3542700.3542709"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE51399.2021.00046"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.14778\/3587136.3587146"},{"key":"e_1_2_1_4_1","doi-asserted-by":"crossref","unstructured":"Kai Han Yunhe Wang Hanting Chen Xinghao Chen Jianyuan Guo Zhenhua Liu Yehui Tang An Xiao Chunjing Xu Yixing Xu et al. 2022. A survey on vision transformer. IEEE transactions on pattern analysis and machine intelligence 45 1 (2022) 87--110.","DOI":"10.1109\/TPAMI.2022.3152247"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.322"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/MCSE.2007.55"},{"key":"e_1_2_1_7_1","volume-title":"Proceedings of NAACL-HLT. 4171--4186","author":"Ming-Wei Chang Jacob Devlin","year":"2019","unstructured":"Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of NAACL-HLT. 4171--4186."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3588689"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3173574.3173962"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.14778\/3192965.3192973"},{"key":"e_1_2_1_11_1","volume-title":"28th Web Conference (WebConf","author":"Noy Natasha","year":"2019","unstructured":"Natasha Noy, Matthew Burgess, and Dan Brickley. 2019. Google Dataset Search: Building a search engine for datasets in an open Web ecosystem. In 28th Web Conference (WebConf 2019)."},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3458456"},{"key":"e_1_2_1_13_1","volume-title":"Attention is all you need. Advances in neural information processing systems 30","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017)."}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3685800.3685857","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T05:28:19Z","timestamp":1735622899000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3685800.3685857"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8]]},"references-count":13,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2024,8]]}},"alternative-id":["10.14778\/3685800.3685857"],"URL":"https:\/\/doi.org\/10.14778\/3685800.3685857","relation":{},"ISSN":["2150-8097"],"issn-type":[{"type":"print","value":"2150-8097"}],"subject":[],"published":{"date-parts":[[2024,8]]},"assertion":[{"value":"2024-11-08","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}