Introduction to the special issue dedicated to the Journal Track of ECML PKDD 2017
This special issue contains papers accepted by the ECML PKDD 2017 Journal Track for publication in the Data Mining and Knowledge Discovery journal. Since 2013, the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery has run a Journal Track in addition to the standard conference paper submissions track. Journal Track papers extend the scope of the conference and are published either in this special issue of Data Mining and Knowledge Discovery or in a special issue of Machine Learning. Papers accepted for the Journal Track are also presented at the conference, just like the papers published in the regular conference proceedings. Papers accepted to this year’s Journal Track are presented by their authors at ECML PKDD 2017 in Skopje, Macedonia, September 18–22, 2017.
The Journal Track of the ECML PKDD series is special in two aspects. First, it solicits submissions that are not only intriguing, novel and on the cutting-edge of science just like conference papers, but also contain substantial, completed and mature work as expected for a regular journal paper. The second aspect to note is the Journal Track’s efficiency: the first decisions on manuscripts were sent (on average) within 8 weeks from submission. The efficient cycle was maintained particularly for the first 12 cut-off dates. In total, 87 manuscripts were submitted to the ECML PKDD 2017 Journal Track, targeting the Data Mining and Knowledge Discovery journal. From them, 9 were accepted in time for printing of this special issue. The special issue also includes 5 manuscripts accepted to the Data Mining and Knowledge Discovery journal from the previous ECML PKDD Journal Tracks: the reviewing of these papers finished after the printing deadline of the previous tracks.
In sum, this issue includes an exciting and diverse set of papers. We hope that the readers will enjoy them as much as the attendees of ECML PKDD 2017 will. We want to thank all the authors of the submitted papers for considering this Journal Track as a venue for publishing their latest work. We also want to thank the active members of the ECML PKDD 2017 Guest Editorial Board and all additional reviewers for the time and energy they invested in putting this special issue together. Many thanks go also to the other chairs of ECML PKDD 2017: it was a pleasure working toward the success of the ECML PKDD 2017 edition with them! Further thanks go to the Editors-in-chief of the two journals, Johannes Fürnkranz (Data Mining and Knowledge Discovery journal) and Peter Flach (Machine Learning Journal), and to the Springer officers of both journals Anandhi Shankar (Data Mining and Knowledge Discovery journal) and Venkat Ganesan (Machine Learning Journal) and to Melissa Fearon, the Senior Editor of Springer and responsible coordinator for both journals, for their support in running the Journal Track smoothly.