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.
Dates and Deadlines
- Abstract Submission Deadline: 30 March 2022
- Paper Submission Deadline: 6 April 2022
- Author Notification: 14 June 2022
- Camera Ready Submission: 1 July 2022
The deadline on each of these dates is 23:59 (AoE).
Submission site and Paper Format
Submission site: https://cmt3.research.microsoft.com/ECMLPKDD2022
Papers should be prepared in English and structured in accordance with the Springer LNCS requirements. Author instructions, style files, and the copyright form are all available for download here.
In this format, papers can be up to 16 pages long (with references). Papers that are too long will be rejected without consideration (papers with smaller than specified page margins and font sizes will be treated as over-length).
Additional resources (e.g., proofs, audio, photos, video, data, or source code) of up to 10 MB in size can be included with your submission. The reviewers and program committee reserve the right to assess the paper purely on the basis of the 16 pages of the article; looking at any extra information is optional and at the discretion of the reviewers.
Double-blind Reviewing Process
Three reviewers will assess each submission for novelty, technical excellence, potential impact, and clarity.
At least one reviewer from industry will be assigned to the applied data science track. ECML PKDD has a long history of being a conference that covers a wide range of Machine Learning and Data Mining subjects. To preserve this, the selection procedure takes into account the range of themes.
The review procedure is double-blind (reviewers and area chairs do not know who the authors are; reviewers do see each other’s names). Papers must not contain any author identifying information (names, affiliations, etc. ), self-references, or links (e.g., github, Youtube) that reveal the authors’ identities (e.g., references to own work should be given neutrally like other references, without mentioning ‘our previous work’ or similar).
However, we realize that there are limitations to what can be done in terms of anonymization; for instance, if you utilize data from your own company and it is important to the article, you may mention the company.
At least one author from each accepted article must attend the conference and present the paper. Please make your travel plans as soon as possible and be aware of any possible immigration procedures (e.g., visa).
Springer’s Lecture Notes in Computer Science Series will publish the conference proceedings (LNCS). Only papers that are presented at the conference will be included in the proceedings. At the time of the conference, online copies of the papers will be made accessible.
Reproducible Research Papers
Authors are strongly encouraged to adhere to the best practices of Reproducible Research (RR), by making available data and software tools for reproducing the results reported in their papers. Authors may flag their submissions as RR and make software and data accessible to reviewers who will verify the accessibility of software and data. Links to data and code must be inserted in the final version of RR papers. For accepted papers, we require the use of standard repository hosting services such as Dataverse, mldata.org, OpenML, figshare, or Zenodo for data sets, and mloss.org, Bitbucket, GitHub, or figshare (where it is possible to assign a DOI) for source code. If data or code gets updated after the paper is published, it is important to enable researchers to access the versions that were used to produce the results reported in the paper. Authors who do not have a preferred repository are advised to consult Springer Nature’s list of repositories and research data policy.
Best Paper Awards
Two student best research paper prizes will be awarded at the conference sponsored by Springer’s Data Mining and Machine Learning journals. In order to be eligible for these awards, the first author of the paper needs to have been a (PhD) student on the day of the submission deadline: April 6, 2022.
Dual Submission Policy
Papers submitted should report original work. Papers that are identical or substantially similar to papers that have been published or submitted elsewhere may not be submitted to ECML PKDD, and the organizers will reject such papers without review. Authors are also not allowed to submit their papers elsewhere during the review period. The dual submission policy applies during the period March 30, 2022 – June 14, 2022.
Submitting unpublished technical reports available online (such as on arXiv), or papers presented in workshops without formal proceedings, is allowed, but such reports or presentations should not be cited to preserve anonymity.
The author list as submitted with the paper is considered final. No changes to this list may be made after paper submission, either during the review period, or in case of acceptance, at the final camera-ready stage.
Conflicts of Interest
During the submission process, you must enter the email domains of all institutions with which you have an institutional conflict of interest. You have an institutional conflict of interest if you are currently employed or have been employed at this institution in the past three years, or you have extensively collaborated with this institution within the past three years. Authors are also required to identify all Program Committee Members and Area Chairs with whom they have a conflict of interest. Examples of conflicts of interest include: co-authorship in the last five years, colleague in the same institution within the last three years, and advisor/student relations.