Data Cleaning - Ihab F. Ilyas

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transformation solutions, so there might be an infinite number of applicable transformation programs. Although the desired transformation might be clear to a human expert, running all of these possibilities is expensive. Thus, practical data transformation tools must effectively prune the space interactively to minimize cost.

      1.2.4 Rule-Based Data Cleaning

      Another common way to detect and rectify errors in databases is through the enforcement of data quality rules, often expressed as integrity constraints (ICs) [Chu et al. 2013b, Kolahi and Lakshmanan 2009, Fan and Geerts 2012]. An example data quality is “For two employees working at the same branch of a company, the senior employee cannot have less vacation time than the junior employee.” Given a dataset, data quality rules can either be manually designed by domain experts who understand the semantics of the data, or be automatically mined from the data [Chu et al. 2013a, Chiang and Miller 2008]. In this context, (qualitative) error detection is the process of identifying violations of ICs, namely, subsets of records that cannot co-exist, and error repair is the exercise of modifying the database, such that the violations are resolved and the new data conforms to the given ICs.

      Rule-based data cleaning techniques using denial constraints [Chu et al. 2013a, Chu et al. 2013b] have been proven to be extremely valuable in detecting and repairing errors in real data in consultation with animal scientists at UC Berkeley studying the effects of firewood cutting on small terrestrial vertebrates (birds, amphibians, reptiles, and small mammals) [Tietje et al. 2018]. Each record in the dataset contains information about capturing an animal, including the time and the location of the capture, the tag ID of the animal captured, and the properties of the animal at the time of the capture, such as weight, species type, gender, and age group. The dataset was collected over the past 20 years. Since every capture was first recorded in a log book and then later transcribed into Excel sheets, there are missing values, typos, and transcribing errors in the dataset. A dozen discovered data quality rules, expressed in the form of denial constraints [Baudinet et al. 1999], are used to detect and repair data errors. This helped the animal data scientists to correct hundreds of errors that would otherwise be very difficult to spot by humans.

      Deriving a comprehensive set of integrity constraints that accurately reflects an organization’s policies and domain semantics is a difficult task. Data is often scattered across silos; for example, different departments may keep their personnel records separate and may have different policies to determine an employee’s salary. Furthermore, data quality rules of varying expressiveness can be found in an organization, ranging from ad-hoc program scripts to simple sanity checks. Rather than consulting domain experts, techniques to automatically discover ICs can be used, which need to balance the trade-off between the expressiveness of the ICs and the efficiency of discovery. Given a set of defined ICs and their violations in a dirty data set, the number of possible ways to update the data to solve the violations is often too large for humans to examine exhaustively. Thus, data cleaning algorithms must be able to search through the huge space of possible updates efficiently to suggest the most plausible updates.

      1.2.5 ML-Guided Cleaning

      One can easily recognize, from the discussion so far, that ML is a natural tool to use in several tasks: classifying duplicate pairs in deduplication, estimating the most likely value for a missing value, predicting a transformation, or classifying values as normal or outliers. Indeed, several of the proposals that we discuss in this book use ML as a component in the proposed cleaning pipeline. We describe these in the corresponding chapters, and in Chapter 7 detail the use of ML for cleaning. However, with the rapid advancement in scaling inference and deploying large-scale ML models, new opportunities arise, namely, treating data cleaning as an ML task (mainly statistical inference), and dealing with all the aforementioned problems holistically. HoloClean [Rekatsinas et al. 2017a] is one of the first ML-based holistic cleaning frameworks, built on a probabilistic model of unclean databases [De Sa et al. 2019]. We discuss this direction in detail in Chapter 7.

      1.2.6 Human-Involved Cleaning

      It is usually impossible for machines to clean data errors with 100% confidence; therefore, human experts are often consulted in a data cleaning workflow to resolve various kinds of uncertainties whenever possible and feasible. For example, the metadata (e.g., keys and integrity constraints) generated by the discovery and profiling step need to be verified by humans before they can be used for detecting and repairing errors, since the metadata is discovered based on one instance of the data set and may not necessarily hold on any data instance of the same schema (e.g., future data). Another example of “judicious” human involvement is in the error detection step: to build a high-quality classifier to determine whether a pair of records are duplicates, we need enough training data, namely, labeled records (as duplicates or as non-duplicates). However, there are far more non-duplicate record pairs than duplicate record pairs in a data set; asking humans to label a random set of record pairs would lead to a set of unbalanced training examples. Therefore, humans need to consulted in an intelligent way to solicit training examples that are most beneficial to the classifier.

      Humans are also needed in the error repair step: data updates generated by automatic data repair techniques are mostly based on heuristics and/or statistics, such as minimal repair distance or maximum likelihood estimation, and thus are not necessarily correct repairs. Therefore, they must be verified by humans before they can be applied to a data set. We highlight when and how humans need to be involved when we discuss various cleaning proposals in this book.

      We present the details of current proposals addressing the topics discussed earlier in multiple chapters organized along two main axes, as shown in Figure 1.2: (1) task: detection vs. repair; and (2) approach: qualitative vs. quantitative. For example, outlier detection is a quantitative error detection task (Chapter 2); data deduplication is a qualitative data cleaning task (Chapter 3), which has both detection of duplicates and repair (also known as record consolidation); data transformation is considered a repair task (Chapter 4) that can be used to fix many types of errors, including outliers and duplicates; and rule-based data cleaning is a qualitative cleaning task (Chapter 6). In addition, a discovery step is usually necessary to obtain data quality rules necessary for rule-based cleaning (Chapter 5). Finally, we include a chapter that discusses the recent advances in ML and data cleaning (Chapter 7) spanning both dimensions.

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      Due to the diverse challenges of data cleaning tasks, different cleaning solutions draw principles and tools from many fields, including statistics, formal logic systems such as first-order logic, distributed data processing, data mining, and ML. Although a basic familiarity with these areas should facilitate understanding of this book, we try to include necessary background and references whenever appropriate.

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