Data quality is crucial in machine learning (ML) applications, as errors in the data can significantly impact the prediction accuracy of the underlying ML model. Therefore, data cleaning is an integral component of any ML pipeline. However, in practical scenarios, data cleaning incurs significant costs, as it often involves domain experts for configuring and executing the cleaning process. Thus, efficient resource allocation during data cleaning can enhance ML prediction accuracy while controlling expenses. This paper presents COMET, a system designed to optimize data cleaning efforts for ML tasks. COMET gives step-by-step recommendations on which feature to clean next, maximizing the efficiency of data cleaning under resource constraints. We evaluated COMET across various datasets, ML algorithms, and data error types, demonstrating its robustness and adaptability. Our results show that COMET consistently outperforms feature importance-based, random, and another well-known cleaning method, achieving up to 52 and on average 5 percentage points higher ML prediction accuracy than the proposed baselines.

Modern artificial intelligence (AI) applications require large quantities of training and test data. This need creates critical challenges not only concerning the availability of such data, but also regarding its quality. For example, incomplete, erroneous, or inappropriate training data can lead to unreliable models that produce ultimately poor decisions. Trustworthy AI applications require high-quality training and test data along many quality dimensions, such as accuracy, completeness, and consistency. We explore empirically the relationship between six data quality dimensions and the performance of 19 popular machine learning algorithms covering the tasks of classification, regression, and clustering, with the goal of explaining their performance in terms of data quality. Our experiments distinguish three scenarios based on the AI pipeline steps that were fed with polluted data: polluted training data, test data, or both. We conclude the paper with an extensive discussion of our observations.

Column headers are among the most relevant types of meta-data for relational tables, because they provide meaning and context in which the data is to be interpreted. Headers play an important role in many data integration, exploration, and cleaning scenarios, such as schema matching, knowledge base augmentation, and similarity search. Unfortunately, in many cases column headers are missing, because they were never defined properly, are meaningless, or have been lost during data extraction, transmission, or storage. For example, around one third of the tables on the Web have missing headers. Missing headers leave abundant tabular data shrouded and inaccessible to many data-driven applications.We introduce a fully automated, multi-phase system that discovers table column headers for cases where headers are missing, meaningless, or unrepresentative for the column values. It leverages existing table headers from web tables to suggest human-understandable, representative, and consistent headers for any target table. We evaluate our system on tables extracted from Wikipedia. Overall, 60% of the automatically discovered table headers are exact and complete. Considering more header candidates, top-5 for example, increases this percentage to 72%.

Inclusion dependencies are an important type of metadata in relational databases, because they indicate foreign key relationships and serve a variety of data management tasks, such as data linkage, query optimization, and data integration. The discovery of inclusion dependencies is, therefore, a well-studied problem and has been addressed by many algorithms. Each of these discovery algorithms follows its own strategy with certain strengths and weaknesses, which makes it difficult for data scientists to choose the optimal algorithm for a given profiling task.

Functional dependencies (FDs) play an important role in maintaining data quality. They can be used to enforce data consistency and to guide repairs over a database. In this work, we investigate the problem of missing values and its impact on FD discovery. When using existing FD discovery algorithms, some genuine FDs could not be detected precisely due to missing values or some non-genuine FDs can be discovered even though they are caused by missing values with a certain NULL semantics. We define a notion of genuineness and propose algorithms to compute the genuineness score of a discovered FD. This can be used to identify the genuine FDs among the set of all valid dependencies that hold on the data. We evaluate the quality of our method over various real-world and semi-synthetic datasets with extensive experiments. The results show that our method performs well for relatively large FD sets and is able to accurately capture genuine FDs.

Data preparation and data profiling comprise many both basic and complex tasks to analyze a dataset at hand and extract metadata, such as data distributions, key candidates, and functional dependencies. Among the most important types of metadata is the number of distinct values in a column, also known as the zeroth-frequency moment. Cardinality estimation itself has been an active research topic in the past decades due to its many applications. The aim of this paper is to review the literature of cardinality estimation and to present a detailed experimental study of twelve algorithms, scaling far beyond the original experiments. First, we outline and classify approaches to solve the problem of cardinality estimation - we describe their main idea, error-guarantees, advantages, and disadvantages. Our experimental survey then compares the performance all twelve cardinality estimation algorithms. We evaluate the algorithms' accuracy, runtime, and memory consumption using synthetic and real-world datasets. Our results show that different algorithms excel in different in categories, and we highlight their trade-offs.