Data Science and Knowledge Discovery

Dear Colleagues,

Data Science and Knowledge Discovery, often using Artificial Intelligence (AI), Machine Learning (ML) including Deep Learning (DL), and Data Mining (DM), are among the most exciting and rapidly growing research fields today. In recent years, they have been successfully used to solve practical problems in virtually every domain, such as engineering, healthcare, manufacturing, energy, transportation, education, and finance.

In this era of Big Data, considerable research is being focused on designing efficient Data Science methods. Nonetheless, practical applications of Knowledge Discovery face several challenges, with examples such as dealing with either too small or too big data, missing and uncertain data, highly multidimensional data, and the need for interpretable models that can provide trustable evidence and explanations of the predictions they make. Therefore, there is a need to develop methods that sift through large amounts of streaming data and extract useful high-level knowledge from there, without human supervision or with very little of it. In addition, learning and obtaining good generalization from fewer training examples, efficient data/knowledge representation schemes, knowledge transfer between tasks and domains, and learning to adapt to varying contexts are also examples of important research problems.

We invite authors from academia, industry, public sector and more to contribute high-quality papers to the Topic, including but not limited to, novel methodological developments, experimental and comparative studies, surveys, application-relevant results, and advances of Data Science, Knowledge Discovery, Artificial Intelligence and Machine Learning fields. Submitted papers can focus on any applications related the participating journals. This Topic welcomes the submission of technical, experimental and methodological papers, both theoretical and solving real-world problems, or proposing practically-relevant systems, as well as on general applications.

Prof. Dr. Sławomir Nowaczyk
Dr. Rita P. Ribeiro
Prof. Dr. Grzegorz Nalepa
Topic Editors