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.
1
Introduction
Enterprises have been acquiring large amounts of data from a variety of sources in order to build large data repositories that power their applications, with the goal of enabling richer and more informed analytics. Data collection and acquisition often introduce errors in data, e.g., missing values, typos, mixed formats, replicated entries for the same real-world entity, and violations of business and data integrity rules. A survey about the state of data science and machine learning (ML) reveals that dirty data is the most common barrier faced by workers dealing with data.1 With the popularity of data science, it has become increasingly evident that data curation, unification, preparation, and cleaning are key enablers in unleashing the value of data.2 According to another survey of about 80 data scientists conducted by CrowdFlower and published in Forbes,3 data scientists spend more than 60% of their time in cleaning and organizing data, and 57% of data scientists regard cleaning and organizing data as the least enjoyable part of their work. Not surprisingly, developing effective and efficient data cleaning solutions is challenging and is rife with deep theoretical and engineering problems.
Regardless of the type of data errors to be fixed, data cleaning activities usually consist of two phases: (1) error detection, where various errors and violations are identified and possibly validated by experts; and (2) error repair, where updates to the database are applied (or suggested to human experts) to bring the data to a cleaner state suitable for downstream applications and analytics. Error detection techniques can be either quantitative or qualitative. Specifically, quantitative error detection techniques often involve statistical methods to identify abnormal behaviors and errors [Hellerstein 2008] (e.g., “a salary that is three standard deviations away from the mean salary is an error”), and hence have been mostly studied in the context of outlier detection [Aggarwal 2013]. On the other hand, qualitative error detection techniques rely on descriptive approaches to specify patterns or constraints of a consistent data instance, and for that reason these techniques identify those data that violate such patterns or constraints as errors. For example, in a descriptive statement about a company HR database, “for two employees working at the same branch of the company, the senior employee cannot earn less salary than the junior employee,” if we find two employees with a violation of the rule, it is likely that there is an error in at least one of them.
Various surveys and books detail specific aspects of data quality and data cleaning. For example, Rahm and Do [2000] classify different types of errors occurring in an Extract-Transform-Load (ETL) process, and survey the tools available for cleaning data in an ETL process. Some work focuses on the effect of incompleteness data on query answering [Grahne 1991] and the use of a Chase procedure [Maier et al. 1979] for dealing with incomplete data [Greco et al. 2012]. Hellerstein [2008] focuses on cleaning quantitative numerical data using mainly statistical techniques. Bertossi [2011] provides complexity results for repairing inconsistent data and performing consistent query answering on inconsistent data. Fan and Geerts [2012] discuss the use of data quality rules in data consistency, data currency, and data completeness, and their interactions. Dasu and Johnson [2003] summarize how techniques in exploratory data mining can be integrated with data quality management. Ganti and Sarma [2013] focus on an operator-centric approach for developing a data cleaning solution, involving the development of customizable operators that can be used as building blocks for developing common solutions. Ilyas and Chu [2015] provide taxonomies and example algorithms for qualitative error detection and repairing techniques. Multiple surveys and tutorials have been published to summarize different definitions of outliers and the algorithms for detecting them [Hodge and Austin 2004, Chandola et al. 2009, Aggarwal 2013]. Data deduplication, a long-standing problem that has been studied for decades [Fellegi and Sunter 1969], has also been extensively surveyed [Koudas et al. 2006, Elmagarmid et al. 2007, Herzog et al. 2007, Dong and Naumann 2009, Naumann and Herschel 2010, Getoor and Machanavajjhala 2012].
This book, however, focuses on the end-to-end data cleaning process, describing various error detection and repair methods, and attempts to anchor these proposals with multiple taxonomies and views. Our goals are (1) to allow researchers and general readers to understand the scope of current techniques and highlight gaps and possible new directions of research; and (2) to give practitioners and system implementers a variety of choices and solutions for their data cleaning activities. In what follows, we give a brief overview of the book’s scope as well as a chapter outline.
Figure 1.1 A typical data cleaning workflow with an optional discovery step, error detection step, and error repair step.
1.1 Data Cleaning Workflow
Figure 1.1 shows a typical data cleaning workflow, consisting of an optional discovery and profiling step, an error detection step, and an error repair step. To clean a dirty dataset, we often need to model various aspects of this data, e.g., schema, patterns, probability distributions, and other metadata. One way to obtain such metadata is by consulting domain experts, typically a costly and time-consuming process. The discovery and profiling step is used to discover these metadata automatically. Given a dirty dataset and the associated metadata, the error detection step finds part of the data that does not conform to the metadata,