{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T10:07:33Z","timestamp":1790676453985,"version":"4.1.0"},"reference-count":57,"publisher":"Association for Computing Machinery (ACM)","issue":"9","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2022,5]]},"abstract":"<jats:p>\n            Data summarization is the process of producing interpretable and representative subsets of an input dataset. It is usually performed following a one-shot process with the purpose of finding the best summary. A useful summary contains\n            <jats:italic>k individually uniform<\/jats:italic>\n            sets that are\n            <jats:italic>collectively diverse<\/jats:italic>\n            to be representative. Uniformity addresses interpretability and diversity addresses representativity. Finding such as summary is a difficult task when data is highly diverse and large. We examine the applicability of Exploratory Data Analysis (EDA) to data summarization and formalize Eda4Sum, the problem of guided exploration of data summaries that seeks to sequentially produce connected summaries with the goal of maximizing their cumulative utility. Eda4Sum generalizes one-shot summarization. We propose to solve it with one of two approaches: (i) Top1Sum that chooses the most useful summary at each step; (ii) RLSum that trains a policy with Deep Reinforcement Learning that rewards an agent for finding a diverse and new collection of uniform sets at each step. We compare these approaches with one-shot summarization and top-performing EDA solutions. We run extensive experiments on three large datasets. Our results demonstrate the superiority of our approaches for summarizing very large data, and the need to provide guidance to domain experts.\n          <\/jats:p>","DOI":"10.14778\/3538598.3538603","type":"journal-article","created":{"date-parts":[[2022,7,27]],"date-time":"2022-07-27T17:12:31Z","timestamp":1658941951000},"page":"1798-1807","source":"Crossref","is-referenced-by-count":17,"title":["Guided exploration of data summaries"],"prefix":"10.14778","volume":"15","author":[{"given":"Brit","family":"Youngmann","sequence":"first","affiliation":[{"name":"MIT CSAIL"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sihem","family":"Amer-Yahia","sequence":"additional","affiliation":[{"name":"CNRS, Univ. Grenoble Alpes"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aurelien","family":"Personnaz","sequence":"additional","affiliation":[{"name":"CNRS, Univ. 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In 2021 IEEE 37th International Conference on Data Engineering (ICDE). IEEE, 1667--1678."},{"key":"e_1_2_1_26_1","volume-title":"Ahmed Abdelkafi, Anna Liu, and Yanlei Diao.","author":"Huang Enhui","year":"2018","unstructured":"Enhui Huang , Liping Peng , Luciano Di Palma , Ahmed Abdelkafi, Anna Liu, and Yanlei Diao. 2018 . Optimization for active learning-based interactive database exploration. pVLDB Endow . (2018). Enhui Huang, Liping Peng, Luciano Di Palma, Ahmed Abdelkafi, Anna Liu, and Yanlei Diao. 2018. Optimization for active learning-based interactive database exploration. pVLDB Endow. (2018)."},{"key":"e_1_2_1_27_1","volume-title":"Interactive data exploration with smart drill-down","author":"Joglekar Manas","year":"2017","unstructured":"Manas Joglekar , Hector Garcia-Molina , and Aditya Parameswaran . 2017. Interactive data exploration with smart drill-down . IEEE TKDE ( 2017 ). Manas Joglekar, Hector Garcia-Molina, and Aditya Parameswaran. 2017. Interactive data exploration with smart drill-down. IEEE TKDE (2017)."},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.5555\/1622737.1622748"},{"key":"e_1_2_1_29_1","volume-title":"Summarizing Hierarchical Multidimensional Data. In 2020 IEEE 36th International Conference on Data Engineering (ICDE). IEEE, 877--888","author":"Kim Alexandra","year":"2020","unstructured":"Alexandra Kim , Laks VS Lakshmanan , and Divesh Srivastava . 2020 . Summarizing Hierarchical Multidimensional Data. In 2020 IEEE 36th International Conference on Data Engineering (ICDE). IEEE, 877--888 . Alexandra Kim, Laks VS Lakshmanan, and Divesh Srivastava. 2020. Summarizing Hierarchical Multidimensional Data. In 2020 IEEE 36th International Conference on Data Engineering (ICDE). 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Towards a benefit-based optimizer for Interactive Data Analysis. In EDBT\/ICDT.  Patrick Marcel Nicolas Labroche and Panos Vassiliadis. 2019. Towards a benefit-based optimizer for Interactive Data Analysis. In EDBT\/ICDT."},{"key":"e_1_2_1_34_1","doi-asserted-by":"crossref","unstructured":"Tova Milo and Amit Somech. 2018. Next-step suggestions for modern interactive data analysis platforms. In KDD. 576--585.  Tova Milo and Amit Somech. 2018. Next-step suggestions for modern interactive data analysis platforms. In KDD. 576--585.","DOI":"10.1145\/3219819.3219848"},{"key":"e_1_2_1_35_1","volume-title":"Proceedings of the 33nd International Conference on Machine Learning, ICML 2016","author":"Mnih Volodymyr","year":"2016","unstructured":"Volodymyr Mnih , Adri\u00e0 Puigdom\u00e8nech Badia , Mehdi Mirza , Alex Graves , Timothy P. Lillicrap , Tim Harley , David Silver , and Koray Kavukcuoglu . 2016 . Asynchronous Methods for Deep Reinforcement Learning . In Proceedings of the 33nd International Conference on Machine Learning, ICML 2016 , New York City, NY, USA , June 19-24, 2016. 1928--1937. Volodymyr Mnih, Adri\u00e0 Puigdom\u00e8nech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu. 2016. Asynchronous Methods for Deep Reinforcement Learning. In Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016. 1928--1937."},{"key":"e_1_2_1_36_1","volume-title":"AAAI'96: Workshop on Structural Issues in Planning and Temporal Reasoning. Citeseer.","author":"Pashenkova Elena","year":"1996","unstructured":"Elena Pashenkova , Irina Rish , and Rina Dechter . 1996 . Value iteration and policy iteration algorithms for Markov decision problem . In AAAI'96: Workshop on Structural Issues in Planning and Temporal Reasoning. Citeseer. Elena Pashenkova, Irina Rish, and Rina Dechter. 1996. Value iteration and policy iteration algorithms for Markov decision problem. In AAAI'96: Workshop on Structural Issues in Planning and Temporal Reasoning. Citeseer."},{"key":"e_1_2_1_37_1","volume-title":"Virtual Event","author":"Personnaz Aur\u00e9lien","year":"2021","unstructured":"Aur\u00e9lien Personnaz , Sihem Amer-Yahia , Laure Berti-\u00c9quille , Maximilian Fabricius , and Srividya Subramanian . 2021 . Balancing Familiarity and Curiosity in Data Exploration with Deep Reinforcement Learning. In aiDM '21: Fourth Workshop in Exploiting AI Techniques for Data Management , Virtual Event , China , 25 June, 2021. 16--23. Aur\u00e9lien Personnaz, Sihem Amer-Yahia, Laure Berti-\u00c9quille, Maximilian Fabricius, and Srividya Subramanian. 2021. Balancing Familiarity and Curiosity in Data Exploration with Deep Reinforcement Learning. In aiDM '21: Fourth Workshop in Exploiting AI Techniques for Data Management, Virtual Event, China, 25 June, 2021. 16--23."},{"key":"e_1_2_1_38_1","volume-title":"DORA THE EXPLORER: Exploring Very Large Data With Interactive Deep Reinforcement Learning. In CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event","author":"Personnaz Aur\u00e9lien","year":"2021","unstructured":"Aur\u00e9lien Personnaz , Sihem Amer-Yahia , Laure Berti-\u00c9quille , Maximilian Fabricius , and Srividya Subramanian . 2021 . DORA THE EXPLORER: Exploring Very Large Data With Interactive Deep Reinforcement Learning. In CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event , Queensland, Australia, November 1 - 5 , 2021, Gianluca Demartini, Guido Zuccon, J. Shane Culpepper, Zi Huang, and Hanghang Tong (Eds.). ACM, 4769--4773. Aur\u00e9lien Personnaz, Sihem Amer-Yahia, Laure Berti-\u00c9quille, Maximilian Fabricius, and Srividya Subramanian. 2021. DORA THE EXPLORER: Exploring Very Large Data With Interactive Deep Reinforcement Learning. In CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1 - 5, 2021, Gianluca Demartini, Guido Zuccon, J. Shane Culpepper, Zi Huang, and Hanghang Tong (Eds.). ACM, 4769--4773."},{"key":"e_1_2_1_39_1","volume-title":"Jeffrey Xu Yu, and Lijun Chang","author":"Qin Lu","year":"2012","unstructured":"Lu Qin , Jeffrey Xu Yu, and Lijun Chang . 2012 . Diversifying top-k results. arXiv preprint arXiv:1208.0076 (2012). Lu Qin, Jeffrey Xu Yu, and Lijun Chang. 2012. Diversifying top-k results. arXiv preprint arXiv:1208.0076 (2012)."},{"key":"e_1_2_1_40_1","volume-title":"Proc. of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2010","author":"Roy Senjuti Basu","year":"2010","unstructured":"Senjuti Basu Roy , Sihem Amer-Yahia , Ashish Chawla , Gautam Das , and Cong Yu . 2010 . Constructing and exploring composite items . In Proc. of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2010 , Indianapolis, Indiana, USA , June 6-10, 2010, Ahmed K. Elmagarmid and Divyakant Agrawal (Eds.). ACM, 843--854. Senjuti Basu Roy, Sihem Amer-Yahia, Ashish Chawla, Gautam Das, and Cong Yu. 2010. Constructing and exploring composite items. In Proc. of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2010, Indianapolis, Indiana, USA, June 6-10, 2010, Ahmed K. Elmagarmid and Divyakant Agrawal (Eds.). ACM, 843--854."},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.5555\/645925.671500"},{"key":"e_1_2_1_42_1","first-page":"531","article-title":"Intelligent rollups in multidimensional OLAP data","volume":"1","author":"Sathe Gayatri","year":"2001","unstructured":"Gayatri Sathe and Sunita Sarawagi . 2001 . Intelligent rollups in multidimensional OLAP data . In VLDB , Vol. 1. 531 -- 540 . Gayatri Sathe and Sunita Sarawagi. 2001. Intelligent rollups in multidimensional OLAP data. In VLDB, Vol. 1. 531--540.","journal-title":"VLDB"},{"key":"e_1_2_1_43_1","volume-title":"Combinatorial optimization: polyhedra and efficiency","author":"Schrijver Alexander","unstructured":"Alexander Schrijver . 2003. Combinatorial optimization: polyhedra and efficiency . Vol. 24 . Springer Science & Business Media . Alexander Schrijver. 2003. Combinatorial optimization: polyhedra and efficiency. Vol. 24. Springer Science & Business Media."},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.14778\/3397230.3397242"},{"key":"e_1_2_1_45_1","volume-title":"IJCAI 2011, Proceedings of the 22nd International Joint Conference on Artificial Intelligence","author":"Shahaf Dafna","year":"2011","unstructured":"Dafna Shahaf and Carlos Guestrin . 2011 . Connecting the Dots between News Articles . In IJCAI 2011, Proceedings of the 22nd International Joint Conference on Artificial Intelligence , Barcelona, Catalonia, Spain , July 16-22, 2011, Toby Walsh (Ed.). IJCAI\/AAAI, 2734--2739. Dafna Shahaf and Carlos Guestrin. 2011. Connecting the Dots between News Articles. In IJCAI 2011, Proceedings of the 22nd International Joint Conference on Artificial Intelligence, Barcelona, Catalonia, Spain, July 16-22, 2011, Toby Walsh (Ed.). IJCAI\/AAAI, 2734--2739."},{"key":"e_1_2_1_46_1","volume-title":"Effortless data exploration with zenvisage: an expressive and interactive visual analytics system. arXiv preprint arXiv:1604.03583","author":"Siddiqui Tarique","year":"2016","unstructured":"Tarique Siddiqui , Albert Kim , John Lee , Karrie Karahalios , and Aditya Parameswaran . 2016. Effortless data exploration with zenvisage: an expressive and interactive visual analytics system. arXiv preprint arXiv:1604.03583 ( 2016 ). Tarique Siddiqui, Albert Kim, John Lee, Karrie Karahalios, and Aditya Parameswaran. 2016. Effortless data exploration with zenvisage: an expressive and interactive visual analytics system. arXiv preprint arXiv:1604.03583 (2016)."},{"key":"e_1_2_1_47_1","unstructured":"Amit Somech Tova Milo and Chai Ozeri. 2019. Predicting \"What is Interesting\" by Mining Interactive-Data-Analysis Session Logs. In EDBT. 456--467.  Amit Somech Tova Milo and Chai Ozeri. 2019. Predicting \"What is Interesting\" by Mining Interactive-Data-Analysis Session Logs. In EDBT. 456--467."},{"key":"e_1_2_1_48_1","volume-title":"Reinforcement learning","author":"Sutton Richard S","unstructured":"Richard S Sutton and Andrew G Barto . 2018. Reinforcement learning . MIT press . Richard S Sutton and Andrew G Barto. 2018. Reinforcement learning. MIT press."},{"key":"e_1_2_1_49_1","volume-title":"IEEE ICDM Workshop on Frequent Itemset Mining Implementations (FIMI)","volume":"126","author":"Uno Takeaki","year":"2004","unstructured":"Takeaki Uno , Masashi Kiyomi , and Hiroki Arimura . 2004 . LCM ver. 2: Efficient mining algorithms for frequent\/closed\/maximal itemsets . In IEEE ICDM Workshop on Frequent Itemset Mining Implementations (FIMI) , Vol. 126 . Takeaki Uno, Masashi Kiyomi, and Hiroki Arimura. 2004. LCM ver. 2: Efficient mining algorithms for frequent\/closed\/maximal itemsets. In IEEE ICDM Workshop on Frequent Itemset Mining Implementations (FIMI), Vol. 126."},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/3335783.3335797"},{"key":"e_1_2_1_51_1","volume-title":"Proceedings of the VLDB Endowment. International Conference on Very Large Data Bases","volume":"11","author":"Wen Yuhao","year":"2018","unstructured":"Yuhao Wen , Xiaodan Zhu , Sudeepa Roy , and Jun Yang . 2018 . Interactive summarization and exploration of top aggregate query answers . In Proceedings of the VLDB Endowment. International Conference on Very Large Data Bases , Vol. 11 . NIH Public Access, 2196. Yuhao Wen, Xiaodan Zhu, Sudeepa Roy, and Jun Yang. 2018. Interactive summarization and exploration of top aggregate query answers. In Proceedings of the VLDB Endowment. International Conference on Very Large Data Bases, Vol. 11. NIH Public Access, 2196."},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1093\/mnras\/stt1458"},{"key":"e_1_2_1_53_1","volume-title":"Voyager: Exploratory analysis via faceted browsing of visualization recommendations","author":"Wongsuphasawat Kanit","year":"2015","unstructured":"Kanit Wongsuphasawat , Dominik Moritz , Anushka Anand , Jock Mackinlay , Bill Howe , and Jeffrey Heer . 2015 . Voyager: Exploratory analysis via faceted browsing of visualization recommendations . IEEE Trans. on Visualization and Computer Graphics ( 2015). Kanit Wongsuphasawat, Dominik Moritz, Anushka Anand, Jock Mackinlay, Bill Howe, and Jeffrey Heer. 2015. Voyager: Exploratory analysis via faceted browsing of visualization recommendations. 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