This is a joint call for the Research and Applied Data Science Tracks.

In research track, we welcome research articles in all fields of machine learning, knowledge discovery, and data mining. Following in the footsteps of ECML PKDD, we are looking for high-quality papers in terms of novelty, technical excellence, potential impact, and presentation clarity. Papers should demonstrate that they make a substantial contribution to the field (e.g, improve the state-of-the-art or provide a new theoretical insight).

In Applied Data Science track, We seek articles that highlight unique applications of machine learning, data mining, and knowledge discovery to real-world problems, bridging the gap between practice and current theory. Papers should explicitly describe the real-world difficulty being addressed (including any idiosyncrasies of the data, such as data set size, noise levels, sample rates, and so on), the methodology employed, and the conclusions derived for the use case.

Submissions will only be evaluated in the track in which they were submitted, and they will not be moved across tracks.