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Philip J. Guo, Sean Kandel, Joseph M. Hellerstein, and Jeffrey Heer. 2011. Proactive wrangling: mixed-initiative end-user programming of data transformation scripts. In Proceedings of the 24th annual ACM symposium on User interface software and technology (UIST ’11). ACM, New York, NY, USA, 65–74. DOI: 10.1145/2047196.2047205.

       Figure 4.8 Z. Abedjan, J. Morcos, I. F. Ilyas, M. Ouzzani, P. Papotti and M. Stonebraker, DataXFormer: A robust transformation discovery system, 2016 IEEE 32nd International Conference on Data Engineering (ICDE), Helsinki, 2016, pp. 1134–1145. DOI: 10.1109/ICDE .2016.7498319.

       Figure 4.9 Based On: Z. Abedjan, J. Morcos, I. F. Ilyas, M. Ouzzani, P. Papotti and M. Stonebraker, DataXFormer: A robust transformation discovery system, 2016 IEEE 32nd International Conference on Data Engineering (ICDE), Helsinki, 2016, pp. 1134–1145. DOI: 10.1109/ICDE.2016.7498319.

       Figure 4.10 Z. Abedjan, J. Morcos, I. F. Ilyas, M. Ouzzani, P. Papotti and M. Stonebraker, DataXFormer: A robust transformation discovery system, 2016 IEEE 32nd International Conference on Data Engineering (ICDE), Helsinki, 2016, pp. 1134–1145. DOI: 10.1109/ICDE .2016.7498319.

       Figure 4.11 Based On: Z. Abedjan, J. Morcos, I. F. Ilyas, M. Ouzzani, P. Papotti and M. Stonebraker, DataXFormer: A robust transformation discovery system, 2016 IEEE 32nd International Conference on Data Engineering (ICDE), Helsinki, 2016, pp. 1134–1145. DOI: 10.1109/ICDE.2016.7498319.

       Figure 5.3 Thorsten Papenbrock and Felix Naumann. 2016. A Hybrid Approach to Functional Dependency Discovery. In Proceedings of the 2016 International Conference on Management of Data (SIGMOD ’16). ACM, New York, NY, USA, 821–833. DOI: 10.1145/2882903.2915203.

       Figure 5.5 Tobias Bleifuß, Sebastian Kruse, and Felix Naumann. 2017. Efficient denial constraint discovery with hydra. Proc. VLDB Endow. 11, 3 (November 2017), 311–323. DOI: 10.14778/3157794.3157800.

       Figure 5.6 Grace Fan, Wenfei Fan, and Floris Geerts. Detecting errors in numeric attributes. In Proc. 15th Int. Conf. on Web-Age Information Management, pages 125–137. Springer, 2014a.

       Figure 5.7 Jiannan Wang and Nan Tang. 2014. Towards dependable data repairing with fixing rules. In Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data (SIGMOD ’14). ACM, New York, NY, USA, 457–468. DOI: 10.1145/2588555.2610494.

       Figure 5.8 Matteo Interlandi and Nan Tang. Proof positive and negative in data cleaning. In Proc. 31st Int. Conf. on Data Engineering, 2015.

       Figure 5.9 Matteo Interlandi and Nan Tang. Proof positive and negative in data cleaning. In Proc. 31st Int. Conf. on Data Engineering, 2015.

       Figure 5.10 Matteo Interlandi and Nan Tang. Proof positive and negative in data cleaning. In Proc. 31st Int. Conf. on Data Engineering, 2015.

       Figure 6.2 Based On: Anup Chalamalla, Ihab F. Ilyas, Mourad Ouzzani, and Paolo Papotti. Descriptive and prescriptive data cleaning. In Proc. ACM SIGMOD Int. Conf. on Management of Data, pages 445–456, 2014. DOI: 10.1145/2588555.2610520.

       Figure 6.3 Alexandra Meliou, Wolfgang Gatterbauer, Suman Nath, and Dan Suciu. 2011. Tracing data errors with view-conditioned causality. In Proceedings of the 2011 ACM SIGMOD International Conference on Management of data (SIGMOD ’11). ACM, New York, NY, USA, 505–516. DOI: 10.1145/1989323.1989376.

       Figure 6.4 Eugene Wu and Samuel Madden. Scorpion: Explaining away outliers in aggregate queries. Proceedings of the VLDB Endowment, Vol. 6, No. 8. Copyright 2013 VLDB Endowment 2150-8097/13/06 553–564.

       Figure 6.5 Anup Chalamalla, Ihab F. Ilyas, Mourad Ouzzani, and Paolo Papotti. Descriptive and prescriptive data cleaning. In Proc. ACM SIGMOD Int. Conf. on Management of Data, pages 445–456, 2014. DOI: 10.1145/2588555.2610520.

       Figure 6.6 Anup Chalamalla, Ihab F. Ilyas, Mourad Ouzzani, and Paolo Papotti. Descriptive and prescriptive data cleaning. In Proc. ACM SIGMOD Int. Conf. on Management of Data, pages 445–456, 2014. DOI: 10.1145/2588555.2610520.

       Figure 6.7 Based On: Anup Chalamalla, Ihab F. Ilyas, Mourad Ouzzani, and Paolo Papotti. Descriptive and prescriptive data cleaning. In Proc. ACM SIGMOD Int. Conf. on Management of Data, pages 445–456, 2014. DOI: 10.1145/2588555.2610520.

       Figure 6.9 Floris Geerts, Giansalvatore Mecca, Paolo Papotti, and Donatello Santoro. That’s all folks! LLUNATIC goes open source. Proceedings of the VLDB Endowment, Vol. 7, No. 13. Copyright 2014 VLDB Endowment 2150-8097/14/08:1565–1568.

       Figure 6.12 Maksims Volkovs, Fei Chiang, Jaroslaw Szlichta, and Rene’e J. Miller. Continuous data cleaning. In Proc. 30th Int. Conf. on Data Engineering, pages 244–255, 2014.

       Figure 6.14 George Beskales, Ihab F. Ilyas, and Lukasz Golab. Sampling the repairs of functional dependency violations under hard constraints. Proc. VLDB Endowment, 3(1–2): 197–207, DOI: 10.14778/1920841.1920870.

       Figure 6.15 Solmaz Kolahi and Laks V. S. Lakshmanan. 2009. On approximating optimum repairs for functional dependency violations. In Proceedings of the 12th International Conference on Database Theory (ICDT ’09), Ronald Fagin (Ed.). ACM, New York, NY, USA, 53–62. DOI: 10.1145/1514894.1514901.

       Figure 6.16 Mohamed Yakout, Ahmed K. Elmagarmid, Jennifer Neville, Mourad Ouzzani, and Ihab F. Ilyas. Guided data repair. Proc. VLDB Endowment, 4(5): 279–289, DOI: 10.14778/1952376.1952378.

       Figure 6.17 Mohamed Yakout, Ahmed K. Elmagarmid, Jennifer Neville, Mourad Ouzzani, and Ihab F. Ilyas. Guided data repair. Proc. VLDB Endowment, 4(5): 279–289, DOI: 10.14778/1952376.1952378.

       Figure 6.18 Wenfei Fan and Floris Geerts. Foundations of Data Quality Management. Synthesis Lectures on Data Management. 2012. © Morgan & Claypool.

       Figure 6.19 Xu Chu, John Morcos, Ihab F. Ilyas, Mourad Ouzzani, Paolo Papotti, Nan Tang, and Yin Ye. 2015. KATARA: A Data Cleaning System Powered by Knowledge Bases and Crowdsourcing. In Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data (SIGMOD ’15). ACM, New York, NY, USA, 1247–1261. DOI: 10.1145/2723372.2749431.

       Figure 6.20 Xu Chu, John Morcos, Ihab F. Ilyas, Mourad Ouzzani, Paolo Papotti, Nan Tang, and Yin Ye. 2015. KATARA: A Data Cleaning System Powered by Knowledge Bases and Crowdsourcing. In Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data (SIGMOD ’15). ACM, New York, NY, USA, 1247–1261. DOI: 10.1145/2723372.2749431.

       Figure 6.23 George Beskales, Ihab F. Ilyas, and Lukasz Golab. Sampling the repairs of functional dependency violations under hard constraints. Proc. VLDB Endowment, 3(1–2): 197–207, DOI: 10.14778/1920841.1920870.

       Figure 7.1 Sunita Sarawagi and Anuradha Bhamidipaty. 2002. Interactive deduplication using active learning. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining (KDD ’02). ACM, New York, NY, USA, 269–278. DOI: 10.1145/775047.775087.

       Figure 7.2 Sidharth Mudgal, Han Li, Theodoros Rekatsinas, AnHai Doan, Youngchoon Park, Ganesh Krishnan, Rohit Deep, Esteban Arcaute, and Vijay Raghavendra. 2018. Deep learning for entity matching: A design space exploration. In Proceedings of the 2018 International Conference on Management of Data (SIGMOD ’18). ACM, New York, NY, USA, 19–34. DOI: 10.1145/3183713 .3196926.

       Figure 7.3 Sidharth Mudgal, Han Li, Theodoros Rekatsinas, AnHai Doan, Youngchoon Park, Ganesh Krishnan, Rohit Deep,

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