Abstract Machine Learning (ML) is integrated into a growing number of systems for various
applications. Because the performance of an ML model is highly dependent on the quality of …

Machine learning (ML) is playing an increasingly important role in data management tasks,
particularly in Data Integration and Preparation (DI&P). The success of ML-based …

Currently, data are often referred to as the oil of the 21st century. This comparison is not only
used to express that the resource data are just as important for the fourth industrial …

Recent work has made significant progress in helping users to automate single data
preparation steps, such as string-transformations and table-manipulation operators (eg …

Software systems that learn from data with machine learning (ML) are used in critical
decision-making processes. Unfortunately, real-world experience shows that the pipelines …

Time series data generated by thousands of sensors are suffering data quality problems.
Traditional constraint-based techniques have greatly contributed to data cleaning …

String data is common in real-world datasets: 67.6% of values in a sample of 1.8 million real
Excel spreadsheets from the web were represented as text. Systems that successfully clean …

Data play a key role in AI systems that support decision-making processes. Data-centric AI
highlights the importance of having high-quality input data to obtain reliable results …

Data quality is critical across many applications. The utility of data is undermined by various
errors, making rigorous data cleaning a necessity. Traditional data cleaning systems depend …

Data preparation is the process of normalizing, cleaning, transforming, and combining data
prior to processing or analysis. It is crucial for obtaining valuable results from data analysis …