This is an overview of the end-to-end data cleaning process. Data quality is one of the most
important problems in data management, since dirty data often leads to inaccurate data …

Data analysis often uses data sets that were collected for different purposes. Indeed, new
insights are often obtained by combining data sets that were produced independently of …

Robotic process automation (RPA) is an emerging technology that allows organizations
automating repetitive clerical tasks by executing scripts that encode sequences of fine …

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 …

In many organizations, it is often challenging for users to find relevant data for specific tasks,
since the data is usually scattered across the enterprise and often inconsistent. In fact, data …

Detecting erroneous values is a key step in data cleaning. Error detection algorithms usually
require a user to provide input configurations in the form of rules or statistical parameters …

Traditional error correction solutions leverage handmaid rules or master data to find the
correct values. Both are often amiss in real-world scenarios. Therefore, it is desirable to …

Data transformation is a critical first step in modern data analysis: before any analysis can be
done, data from a variety of sources must be wrangled into a uniform format that is amenable …

Data preparation is widely recognized as the most time-consuming process in modern
business intelligence (BI) and machine learning (ML) projects. Automating complex data …

Obtaining value from data through analysis often requires significant prior effort on data
preparation. Data preparation covers the discovery, selection, integration and cleaning of …