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Figures

Figure 2.3 Based On: Patrick Wessa. Free statistics software, office for research development and education, version 1.1. 23-r7. http://www.wessa.net, 2012

Figure 2.4 Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng, and Jörg Sander. 2000. LOF: identifying density-based local outliers. SIGMOD Rec. 29, 2 (May 2000), 93–104. DOI: 10.1145/335191.335388.

Figure 2.5 Varun Chandola, Arindam Banerjee, and Vipin Kumar. 2009. Anomaly detection: A survey. ACM Comput. Surv. 41, 3, Article 15 (July 2009), 58 pages. DOI: 10.1145/1541880.1541882.

Figure 2.6 Charu C. Aggarwal. Outlier Analysis. Springer, 2013.

Figure 2.7 Xiuyao Song, Mingxi Wu, Christopher Jermaine, and Sanjay Ranka. Conditional anomaly detection. IEEE Trans. Knowl. and Data Eng., 19(5), 2007.

Figure 3.3 Based On: Jiannan Wang, Guoliang Li, and Jianhua Feng. 2012. Can we beat the prefix filtering?: an adaptive framework for similarity join and search. In Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data (SIGMOD ’12). ACM, New York, NY, USA, 85–96. DOI: 10.1145/2213836.2213847.

Figure 3.6 Jens Bleiholder and Felix Naumann. 2009. Data fusion. ACM Comput. Surv. 41, 1, Article 1 (January 2009), 41 pages. DOI: 10.1145/1456650.1456651.

Figure 3.7 Based On: George Beskales, Mohamed A. Soliman, Ihab F. Ilyas, and Shai Ben-David. Modeling and querying possible repairs in duplicate detection. Proc. VLDB Endowment, 2(1): 598–609, (August 2009), 598–609. DOI: 10.14778/1687627.1687695.

Figure 3.8 Based On: George Beskales, Mohamed A. Soliman, Ihab F. Ilyas, and Shai Ben-David. Modeling and querying possible repairs in duplicate detection. Proc. VLDB Endowment, 2(1): 598–609, (August 2009), 598–609. DOI: 10.14778/1687627.1687695.

Figure 3.11 Jiannan Wang, Tim Kraska, Michael J. Franklin, and Jianhua Feng. Crowder: Crowdsourcing entity resolution. Proc. VLDB Endowment, 5(11): 1483–1494, DOI: 10.14778/2350229.2350263.

Figure 3.12 Jiannan Wang, Tim Kraska, Michael J. Franklin, and Jianhua Feng. Crowder: Crowdsourcing entity resolution. Proc. VLDB Endowment, 5(11): 1483–1494, DOI: 10.14778/2350229.2350263.

Figure 3.13 Chaitanya Gokhale, Sanjib Das, AnHai Doan, Jeffrey F. Naughton, Narasimhan Rampalli, Jude Shavlik, and Xiaojin Zhu. 2014. Corleone: hands-off crowdsourcing for entity matching. In Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data (SIGMOD ’14). ACM, New York, NY, USA, 601–612. DOI: 10.1145/2588555.2588576.

Figure 3.14 Pradap Konda, Sanjib Das, Paul Suganthan GC, AnHai Doan, Adel Ardalan, Jeffrey R. Ballard, Han Li, Fatemah Panahi, Haojun Zhang, Jeff Naughton, et al. Magellan: Toward building entity matching management systems. Proc. VLDB Endowment, 9(12): 1197–1208, 2016.

Figure 3.15 Based on: Michael Stonebraker, Daniel Bruckner, Ihab F. Ilyas, George Beskales, Mitch Cherniack, Stanley B. Zdonik, Alexander Pagan, and Shan Xu. Data curation at scale: The data tamer system. In Proc. 6th Biennial Conf. on Innovative Data Systems Research, 2013. http://cidrdb.org/

Figure 4.3 Vijayshankar Raman and Joseph M. Hellerstein. 2001. Potter’s Wheel: An Interactive Data Cleaning System. In Proceedings of the 27th International Conference on Very Large Data Bases (VLDB ’01), Peter M. G. Apers, Paolo Atzeni, Stefano Ceri, Stefano Paraboschi, Kotagiri Ramamohanarao, and Richard Thomas Snodgrass (Eds.). Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 381–390.

Figure 4.4 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. and Jeffrey Heer, Joseph Hellerstein, and Sean Kandel. Predictive interaction for data transformation. In Proc. 7th Biennial Conf. on Innovative Data Systems Research, 2015. and Sean Kandel, Andreas Paepcke, Joseph Hellerstein, and Jeffrey Heer. 2011. Wrangler: interactive visual specification of data transformation scripts. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI’11). ACM, New York, NY, USA, 3363–3372. DOI: 10.1145/1978942.1979444.

Figure 4.5 Copyright © 2007 Free Software Foundation, Inc. http://fsf.org/, (http://fsf.org/)

Figure 4.6 Sumit Gulwani. 2011. Automating string processing in spreadsheets using input-output examples. In Proceedings of the 38th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages (POPL ’11). ACM, New York, NY, USA, 317–330. DOI: 10.1145/1926385.1926423.

Figure 4.7 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, 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 7.8 Jiannan Wang, Sanjay Krishnan, Michael J. Franklin, Ken Goldberg, Tim Kraska, and Tova Milo. A sample-and-clean framework for fast and accurate query processing on dirty data. In Proc. ACM SIGMOD Int. Conf. on Management of Data, pages 469–480, 2014. DOI: 10.1145/2588555.2610505.

Figure 7.9 Sanjay Krishnan, Jiannan Wang, Eugene Wu, Michael J. Franklin, and Ken Goldberg. Activeclean: Interactive data cleaning for statistical modeling. Proc. VLDB Endowment, 9(12, August 2016): 948–959. DOI: 10.14778/2994509.2994514.

Tables

Table 3.2 Jens Bleiholder and Felix Naumann. 2009. Data fusion. ACM Comput. Surv. 41, 1, Article 1 (January 2009), 41 pages. DOI: 10.1145/1456650.1456651 and Xin Luna Dong and Felix Naumann. Data fusion: resolving data conflicts for integration. Proc. VLDB Endowment, 2(2): 1654–1655, 2009.

Table 4.1 Based On: Sean Kandel, Andreas Paepcke, Joseph Hellerstein, and Jeffrey Heer. 2011. Wrangler: interactive visual specification of data transformation scripts. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’11). ACM, New York, NY, USA, 3363–3372. DOI: 10.1145/1978942.1979444.

Table 5.2 Lukasz Golab, Howard Karloff, Flip Korn, Divesh Srivastava, and Bei Yu. On generating near-optimal tableaux for conditional functional dependencies. Proc. VLDB Endowment, 1(1): 376–390, DOI: 10.14778/1453856.1453900.

Table 6.1 Based On: Xu Chu, Ihab F. Ilyas, and Paolo Papotti. Holistic data cleaning: Putting violations into context. In Proc. 29th Int. Conf. on Data Engineering, pages 458–469, 2013b.

Table 6.3 Wenfei Fan, Jianzhong Li, Shuai Ma, Nan Tang, and Wenyuan Yu. 2011. Interaction between record matching and data repairing. In Proceedings of the 2011 ACM SIGMOD International Conference on Management of data (SIGMOD ’11). ACM, New York, NY, USA, 469–480. DOI: 10.1145/1989323.1989373.

Table 7.1 Theodoros Rekatsinas, Xu Chu, Ihab F. Ilyas, and Christopher Ré. 2017. HoloClean: holistic data repairs with probabilistic inference. Proc. VLDB Endow. 10, 11 (August 2017), 1190–1201. DOI: 10.14778/3137628.3137631.