{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T20:36:42Z","timestamp":1780346202209,"version":"3.54.1"},"reference-count":84,"publisher":"Association for Computing Machinery (ACM)","issue":"1","funder":[{"name":"NSF (National Science Foundation)","award":["CNS-2346555"],"award-info":[{"award-number":["CNS-2346555"]}]},{"name":"Stena Center for Financial Technology","award":["n\/a"],"award-info":[{"award-number":["n\/a"]}]},{"name":"One Utah Data Science Hub","award":["n\/a"],"award-info":[{"award-number":["n\/a"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. ACM Manag. Data"],"published-print":{"date-parts":[[2026,4,2]]},"abstract":"\n Data summarization is essential to discover insights from large datasets. In spreadsheets,\n pivot tables<\/jats:italic>\n offer a convenient way to summarize tabular data by computing aggregates over some attributes, grouped by others. However, identifying attribute combinations that will result in\n useful<\/jats:italic>\n pivot tables remains a challenge, especially for high-dimensional datasets. We formalize the problem of automatically recommending\n insightful<\/jats:italic>\n and\n interpretable<\/jats:italic>\n pivot tables, eliminating the tedious manual process. A crucial aspect of recommending a set of pivot tables is to\n diversify<\/jats:italic>\n them. Traditional work inadequately address the table-diversification problem, which leads us to the problem of\n pivot table diversification<\/jats:italic>\n .\n <\/jats:p>\n \n We present SAGE, a data-\n s<\/jats:underline>\n emantics-\n a<\/jats:underline>\n ware system for recommendin\n g<\/jats:underline>\n k-budgeted diverse pivot tables, overcoming the shortcomings of prior work for top-k recommendations that cause redundancy. SAGE ensures that each pivot table is\n insightful<\/jats:italic>\n ,\n interpretable<\/jats:italic>\n , and\n adaptive<\/jats:italic>\n to the user's actions and preferences, while also guaranteeing that the set of pivot tables are different from each other, offering a diverse recommendation. We make two key technical contributions: (1) a\n data-semantics-aware model<\/jats:italic>\n to measure the utility of a single pivot table and the diversity of a set of pivot tables, and (2) a\n scalable greedy algorithm<\/jats:italic>\n that can efficiently select a set of diverse pivot tables of high utility, by leveraging data semantics to significantly reduce the combinatorial search space. Our extensive experiments on four real-world datasets show that SAGE outperforms alternative approaches, and efficiently scales to accommodate high-dimensional datasets. Additionally, through multiple case studies, we demonstrate SAGE's qualitative superiority over existing tools, and through a user study, we validate its practical usefulness and alignment with user preferences.\n <\/jats:p>","DOI":"10.1145\/3786637","type":"journal-article","created":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T17:54:13Z","timestamp":1775584453000},"page":"1-28","source":"Crossref","is-referenced-by-count":1,"title":["Data-Semantics-Aware Recommendation of Diverse Pivot Tables"],"prefix":"10.1145","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2069-4508","authenticated-orcid":false,"given":"Whanhee","family":"Cho","sequence":"first","affiliation":[{"name":"University of Utah, Salt Lake City, Utah, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5275-7844","authenticated-orcid":false,"given":"Anna","family":"Fariha","sequence":"additional","affiliation":[{"name":"University of Utah, Salt Lake City, Utah, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,4,7]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"2023. 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Accessed: 2025-01--21."},{"key":"e_1_2_1_39_1","first-page":"1","article-title":"Supporting Serendipitous Discovery and Balanced Analysis of Online Product Reviews with Interaction-Driven Metrics and Bias-Mitigating Suggestions. In CHI '22: CHI Conference on Human Factors in Computing Systems, New Orleans, LA, USA, 29 April 2022 - 5 May 2022","volume":"9","author":"Jasim Mahmood","year":"2022","unstructured":"Mahmood Jasim, Christopher Collins, Ali Sarvghad, and Narges Mahyar. 2022. Supporting Serendipitous Discovery and Balanced Analysis of Online Product Reviews with Interaction-Driven Metrics and Bias-Mitigating Suggestions. In CHI '22: CHI Conference on Human Factors in Computing Systems, New Orleans, LA, USA, 29 April 2022 - 5 May 2022. ACM, 9:1--9:24.","journal-title":"ACM"},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2017.2685998"},{"key":"e_1_2_1_41_1","doi-asserted-by":"crossref","unstructured":"William B Johnson Joram Lindenstrauss et al. 1984. 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Schloss Dagstuhl - Leibniz-Zentrum f\u00fcr Informatik, 13:1--13:25."},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF02832348"},{"key":"e_1_2_1_53_1","volume-title":"Weighting methods for multi-criteria decision making technique. Journal of Applied Sciences and Environmental Management 23 (09","author":"Odu Godwin","year":"2019","unstructured":"Godwin Odu. 2019. Weighting methods for multi-criteria decision making technique. Journal of Applied Sciences and Environmental Management 23 (09 2019), 1449."},{"key":"e_1_2_1_54_1","unstructured":"OpenAI. 2025. ChatGPT. https:\/\/chat.openai.com. Accessed: 2025-02--14."},{"key":"e_1_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/1562849.1562855"},{"key":"e_1_2_1_56_1","volume-title":"House Sale Dataset. https:\/\/www.kaggle.com\/datasets\/animeshparikshya\/house-sale-data-81-column. Accessed","author":"Parikshya Animesh","year":"2025","unstructured":"Animesh Parikshya. 2025. House Sale Dataset. https:\/\/www.kaggle.com\/datasets\/animeshparikshya\/house-sale-data-81-column. Accessed 16 July 2025."},{"key":"e_1_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2017.2744198"},{"key":"e_1_2_1_58_1","volume-title":"SIGIR 2006: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Radlinski Filip","year":"2006","unstructured":"Filip Radlinski and Susan T. Dumais. 2006. Improving personalized web search using result diversification. In SIGIR 2006: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Seattle, Washington, USA, August 6--11, 2006. 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Discrete applied mathematics 126, 2--3 (2003), 313--322."},{"key":"e_1_2_1_62_1","unstructured":"Salesforce. 2024. Tableau. https:\/\/www.tableau.com. Accessed: 2025--10--14."},{"key":"e_1_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.5555\/645925.671500"},{"key":"e_1_2_1_64_1","volume-title":"User-Adaptive Exploration of Multidimensional Data. In VLDB 2000, Proceedings of 26th International Conference on Very Large Data Bases, September 10--14","author":"Sarawagi Sunita","year":"2000","unstructured":"Sunita Sarawagi. 2000. User-Adaptive Exploration of Multidimensional Data. In VLDB 2000, Proceedings of 26th International Conference on Very Large Data Bases, September 10--14, 2000, Cairo, Egypt. Morgan Kaufmann, 307--316."},{"key":"e_1_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1007\/s007780100046"},{"key":"e_1_2_1_66_1","volume-title":"6th International Conference on Extending Database Technology","volume":"182","author":"Sarawagi Sunita","year":"1998","unstructured":"Sunita Sarawagi, Rakesh Agrawal, and Nimrod Megiddo. 1998. Discovery-Driven Exploration of OLAP Data Cubes. In Advances in Database Technology - EDBT'98, 6th International Conference on Extending Database Technology, Valencia, Spain, March 23--27, 1998, Proceedings (Lecture Notes in Computer Science, Vol. 1377). Springer, 168--182."},{"key":"e_1_2_1_67_1","volume-title":"Video Game Sales Dataset. https:\/\/www.kaggle.com\/datasets\/gregorut\/videogamesales. Accessed","author":"Smith Gregory","year":"2025","unstructured":"Gregory Smith. 2025. Video Game Sales Dataset. https:\/\/www.kaggle.com\/datasets\/gregorut\/videogamesales. Accessed 16 July 2025."},{"key":"e_1_2_1_68_1","unstructured":"Aofeng Su Aowen Wang Chao Ye Chen Zhou Ga Zhang Guangcheng Zhu Haobo Wang Haokai Xu Hao Chen Haoze Li Haoxuan Lan Jiaming Tian Jing Yuan Junbo Zhao Junlin Zhou Kaizhe Shou Liangyu Zha Lin Long Liyao Li Pengzuo Wu Qi Zhang Qingyi Huang Saisai Yang Tao Zhang Wentao Ye Wufang Zhu Xiaomeng Hu Xijun Gu Xinjie Sun Xiang Li Yuhang Yang and Zhiqing Xiao. 2024. TableGPT2: A Large Multimodal Model with Tabular Data Integration. arXiv:2411.02059 [cs.LG]"},{"key":"e_1_2_1_69_1","volume-title":"Kigali","author":"Syed Aaquib","year":"2023","unstructured":"Aaquib Syed, Phillip Huang Guo, and Vijaykaarti Sundarapandiyan. 2023. Prune and Tune: Improving Efficient Pruning Techniques for Massive Language Models. In The First Tiny Papers Track at ICLR 2023, Tiny Papers @ ICLR 2023, Kigali, Rwanda, May 5, 2023. 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