Data cleaning has played a critical role in ensuring data quality for enterprise applications.
Naturally, there has been extensive research in this area, and many data cleaning …

With the evolution of new technologies, the production of digital data is constantly growing. It
is thus necessary to develop data management strategies in order to handle the large-scale …

Data quality management, especially data cleansing, has been extensively studied for many
years in the areas of data management and visual analytics. In the paper, we first review and …

We introduce a few-shot learning framework for error detection. We show that data
augmentation (a form of weak supervision) is key to training high-quality, ML-based error …

Deep Learning revolutionizes almost all fields of computer science including data
management. However, the demand for high-quality training data is slowing down deep …

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 …

We present RUDIK, a system for the discovery of declarative rules over knowledge-bases
(KBs). RUDIK discovers rules that express positive relationships between entities, such as" if …

We focus on data fusion, ie, the problem of unifying conflicting data from data sources into a
single representation by estimating the source accuracies. We propose SLiMFast, a …

is to understand the dataset at hand and its metadata. The process of metadata discovery is
known as data profiling. Profiling activities range from ad-hoc approaches, such as eye …

Patterns (or regex-based expressions) are widely used to constrain the format of a domain
(or a column), eg, a Year column should contain only four digits, and thus a value like “1980 …