The workshop will be based on invited talks, contributed talks, datasets presentations, posters and demos. In that respect, we have three submission types:
- Unpublished (original) works (max 16 pages excluding references, LNCS proceedings format).
- Recently published works (extended abstract, 4 to 8 pages including references, LNCS proceedings format). The extended abstract has to mention where and when the paper has been published.
- A dataset description together with proposed machine learning task(s) on it (extended abstract, 4 to 12 pages, LNCS format). The authors must explicitly specify it they wish to submit their dataset for the hackathon (such a submission requires that the dataset must be available to the program cmomittee upon request). If dataset is accepted for presentation, authors must agree on the further publication of data and tasks.
Submissions must be made using Easy Chair (you must create an account if you do not have one already)
Papers must be written in English and formatted according to the Springer Lecture Notes in Computer Science (LNCS) guidelines. Authors should consult Springer’s authors’ guidelines and use their proceedings templates, either for LaTeX or for Word, for the preparation of their papers. Springer encourages authors to include their ORCIDs in their papers. In addition, the corresponding author of each original paper, acting on behalf of all of the authors of that paper, must complete and sign a Consent-to-Publish form. By signing this form the copyright for their paper is transferred to Springer.
Accepted original papers will be published as workshop proceedings by Springer as part of the series Lecture Notes in Computer Science. The proceedings of the past ECML-PKDD Workshops are available through SpringerLink.
Papers and datasets will be evaluated according to their originality and relevance to the workshop, and should include author names, affiliations, contact information, and an abstract. Accepted papers have to be presented orally or as a poster, and will be available on the website. Accepted dataset/task for the hackathon will be publicly available, and will benefits from the solutions provided by the attendants at the workshop.
Proceedings will be edited as a digital report, and published either through a scientific publisher in collaboration with LNCS, or on arXiv.
Please note that at least one author of each accepted paper should register for the ECML/PKDD conference.