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Recent work has made significant progress in helping users to automate single data preparation steps, such as string-transformations and table-manipulation operators (e.g., Join, GroupBy, Pivot, etc.).
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Auto-FuzzyJoin: Auto-Tune Fuzzy Joins Without Labeled Examples. In Proceedings of the 2009 ACM SIGMOD International Conference on Management of data . 535–548
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Automating string processing in spreadsheets using input-output examples
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Spreadsheet table transformations from examples. In Proceedings of SIGPLAN . 317–328
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Holistic data cleaning: Putting violations into context. In 2013 IEEE 29th International Conference on Data Engineering (ICDE) . IEEE, 458–469
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Discovering Linkage Points over Web Data
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Playing atari with deep reinforcement learning
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Kevin Ellis, Armando Solar-Lezama, and Josh Tenenbaum. 2015 · 2015
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SEMA-JOIN: joining semantically-related tables using big table corpora
Yeye He, Kris Ganjam, and Xu Chu. 2015 · 2015
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Oliver Lehmberg, Dominique Ritze, Petar Ristoski, Robert Meusel, Heiko Paulheim, and Christian Bizer. 2015 · 2015
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Thorsten Papenbrock, Jens Ehrlich, Jannik Marten, Tommy Neubert, Jan-Peer Rudolph, Martin Schönberg, Jakob Zwiener, and Felix Naumann. 2015 · 2015
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Tom Schaul, John Quan, Ioannis Antonoglou, and David Silver. 2015 · 2015
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DataXFormer: A robust transformation discovery system. In ICDE
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A hybrid approach to functional dependency discovery. In Proceedings of the 2016 International Conference on Management of Data . 821–833
Synthesizing highly expressive SQL queries from input-output examples. In Proceedings of the 38th ACM SIGPLAN Conference on Programming Language Design and Implementation . 452–466
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Auto-join: Joining tables by leveraging transformations
Erkang Zhu, Yeye He, and Surajit Chaudhuri. 2017 · 2017
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Transform-data-by-example (TDE): an extensible search engine for data transformations
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Transform-Data-by-Example (TDE) Extensible Data Transformation in Excel. In Proceedings of the 2018 International Conference on Management of Data . 1785–1788
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Thorsten Papenbrock and Felix Naumann. 2016 · 2016
Cited alongside, same era.
Gartner: Market Guide for Self-Service Data Preparation
Rita L. Sallam, Paddy Forry, Ehtisham Zaidi, and Shubhangi Vashisth. 2016 · 2016
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Blinkfill: Semi-supervised programming by example for syntactic string transformations
Rishabh Singh. 2016 · 2016
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Auto-Pandas code
2017.04.26a · 2017
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C-sharp String Operators
2017.04.26 · 2017
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Jupter Notebooks
2017.04.26 · 2017
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Pandas Aggregate
2017.04.26a · 2017
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Yeye He, Kris Ganjam, Kukjin Lee, Yue Wang, Vivek Narasayya, Surajit Chaudhuri, Xu Chu, and Yudian Zheng. 2018b · 2018
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Auto-detect: Data-driven error detection in tables. In Proceedings of the 2018 International Conference on Management of Data . 1377–1392
Zhipeng Huang and Yeye He. 2018 · 2018
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Zhongjun Jin, Michael Cafarella, HV Jagadish, Sean Kandel, Michael Minar, and Joseph M Hellerstein. 2018 · 2018
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Deep learning for entity matching: A design space exploration. In Proceedings of the 2018 International Conference on Management of Data . 19–34
Sidharth Mudgal, Han Li, Theodoros Rekatsinas, AnHai Doan, Youngchoon Park, Ganesh Krishnan, Rohit Deep, Esteban Arcaute, and Vijay Raghavendra. 2018 · 2018
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Richard S Sutton and Andrew G Barto. 2018 · 2018
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AutoPandas: neural-backed generators for program synthesis
Rohan Bavishi, Caroline Lemieux, Roy Fox, Koushik Sen, and Ion Stoica. 2019 · 2019
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End-to-end entity resolution for big data: A survey
Vassilis Christophides, Vasilis Efthymiou, Themis Palpanas, George Papadakis, and Kostas Stefanidis. 2019 · 2019
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Sairam Gurajada, Lucian Popa, Kun Qian, and Prithviraj Sen. 2019 · 2019
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Holodetect: Few-shot learning for error detection. In Proceedings of the 2019 International Conference on Management of Data . 829–846
Alireza Heidari, Joshua McGrath, Ihab F Ilyas, and Theodoros Rekatsinas. 2019 · 2019
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Incorporating Data Context to Cost-Effectively Automate End-to-End Data Wrangling
Martin Koehler, Edward Abel, Alex Bogatu, Cristina Civili, Lacramioara Mazilu, Nikolaos Konstantinou, Alvaro Fernandes, John Keane, Leonid Libkin, and Norman W Paton. 2019 · 2019
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Uni-Detect: A Unified Approach to Automated Error Detection in Tables. In Proceedings of the 2019 International Conference on Management of Data . 811–828
Pei Wang and Yeye He. 2019 · 2019
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Auto-EM: End-to-end Fuzzy Entity-Matching using Pre-trained Deep Models and Transfer Learning. In The World Wide Web Conference . 2413–2424
Chen Zhao and Yeye He. 2019 · 2019
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Auto-transform: learning-to-transform by patterns
Zhongjun Jin, Yeye He, and Surajit Chauduri. 2020 · 2020
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Semi-supervised data cleaning
Mohammad Mahdavi Lahijani. 2020 · 2020
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Deep entity matching with pre-trained language models
Yuliang Li, Jinfeng Li, Yoshihiko Suhara, AnHai Doan, and Wang-Chiew Tan. 2020 · 2020
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Searching a database of source codes using contextualized code search
Rohan Mukherjee, Swarat Chaudhuri, and Chris Jermaine. 2020 · 2020
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Auto-Suggest: Learning-to-Recommend Data Preparation Steps Using Data Science Notebooks. In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data . 1539–1554
Cong Yan and Yeye He. 2020 · 2020
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SCODED: Statistical constraint oriented data error detection. In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data . 845–860
Jing Nathan Yan, Oliver Schulte, MoHan Zhang, Jiannan Wang, and Reynold Cheng. 2020 · 2020
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Multi-Context Attention for Entity Matching. In Proceedings of The Web Conference 2020 . 2634–2640
Dongxiang Zhang, Yuyang Nie, Sai Wu, Yanyan Shen, and Kian-Lee Tan. 2020 · 2020
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Deep Entity Matching: Challenges and Opportunities
Yuliang Li, Jinfeng Li, Yoshihiko Suhara, Jin Wang, Wataru Hirota, and Wang-Chiew Tan. 2021 · 2021
Closest in time.
Semi-Supervised Data Cleaning with Raha and Baran.. In CIDR
Mohammad Mahdavi and Ziawasch Abedjan. 2021 · 2021
Closest in time.