The adoption of machine learning (ML) and, more specifically, deep learning (DL)
applications into all major areas of our lives is underway. The development of trustworthy AI …

The development of the Internet in recent years has made it possible and useful to access
many different information systems anywhere in the world to obtain information. While there …

Deep learning (DL) based software systems are difficult to develop and maintain in industrial
settings due to several challenges. Data management is one of the most prominent …

For statistical learning, categorical variables in a table are usually considered as discrete
entities and encoded separately to feature vectors, eg, with one-hot encoding.“Dirty” non …

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 …

Process-oriented data mining (process mining) uses algorithms and data (in the form of
event logs) to construct models that aim to provide insights into organisational processes …

Visualization misinformation is a prevalent problem, and combating it requires
understanding people's ability to read, interpret, and reason about erroneous or potentially …

One of the major criticisms of Artificial Intelligence is its lack of explainability. A claim is made
by many critics that without knowing how an AI may derive a result or come to a given …

Time is an exceptional dimension with high relevance in medicine, engineering, business,
science, biography, history, planning, or project management. Understanding time-oriented …

Although research on time series prediction based on deep learning is being actively carried
out in various industries, deep learning technology still has a high entry barrier for …