Research and ADS Tracks
This is a joint call for papers for both the Research Track and the Applied Data Science Track.
In the Research Track, we invite submissions of research papers from all areas of knowledge discovery, data mining and machine learning. Following the tradition of ECML-PKDD, we are looking for high-quality papers in terms of novelty, technical quality, potential impact, and clarity of presentation. Papers should demonstrate that they provide a significant contribution to the field (e.g., improve the state-of-the-art or provide a new theoretical insight).
In the Applied Data Science Track, we invite papers that present innovative applications of knowledge discovery, data mining and machine learning to solve challenging and important real world use cases, thereby bridging the gap between practice and current theory. Papers should clearly explain the real world challenge that is being addressed (including any peculiarities of the data, like the size of the data set, noise levels, sampling rates, etc), the methodology that is being used, and the conclusions that are drawn for the use case.
KEY DATES AND DEADLINES:
- Abstract submission deadline:
March 19, 2020
- Paper Submission Deadline:
March, 26 2020April, 02 2020
- Author notification:
June 4, 2020
- All deadlines expire on 23:59 AoE (UTC - 12).
Papers must be written in English and formatted according to the Springer LNCS guidelines. Author instructions, style files and the copyright form can be downloaded here.
The maximum length of papers is 16 pages (including references) in this format. The program chairs reserve the right to reject any over-length papers without review. Papers that ‘cheat’ the page limit by, including but not limited to, using smaller than specified margins or font sizes will also be treated as over-length. Note that for example negative vspaces are also not allowed by Springer’s formatting guidelines.
Up to 10 MB of additional materials (e.g. proofs, audio, images, video, data, or source code) can be uploaded with your submission. The reviewers and the program committee reserve the right to judge the paper solely on the basis of the 16 pages of the paper; looking at any additional material is at the discretion of the reviewers and is not required.
In contrast to previous years, this year ECML-PKDD will apply a double-blind review process (see also the double-blind reviewing process section below for further details). All papers need to be ‘best-effort’ anonymized. We strongly encourage to also make code and data available anonymously (e.g., in an anonymous git repository or Dropbox folder). It is allowed to have a (non-anonymous) pre-print online, but it should not be cited in the submitted paper to preserve anonymity. Reviewers will be asked not to search for them.
Electronic submissions will be handled via EasyChair (submissions are now open).
Before submitting, please consider carefully what the appropriate track is (see top of page). The track of choice can be indicated in the submission form. Submissions will be assessed in the track where they were submitted and cannot be transferred across tracks.
To submit a paper, please do the following:
- Create an account and log into EasyChair.
- Create a New Paper submission.
- Select the Research Track or the Applied Data Science Track.
- Complete the submission.
Abstracts must be submitted by Thursday March 19, 2020 and full submissions must be submitted by
Thursday March 26, 2020 Thursday April, 02 2020.
DOUBLE-BLIND REVIEWING PROCESS
Submissions will be evaluated by three reviewers on the basis of novelty, technical quality, potential impact, and clarity. For the applied data science track, one reviewer at least will come from industry. ECML-PKDD has a long-standing reputation of a truly diverse conference where many topics in Machine Learning and Data Mining are represented. To maintain this, diversity of topics is also taken into account in the selection process. Submissions will be assessed in the track where they were submitted and will not be transferred across tracks.
The reviewing process is double-blind (author identities are not known by reviewers or area chairs; reviewers do see each other’s names). Papers must not include identifying information of the authors (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, not mentioning ‘our previous work’ or similar). However, we recognize there are limits to what is feasible with respect to anonymization. For example, if you use data from your own organization and it is relevant to the paper to name this organization, you may do so.
For each accepted paper, at least one author must attend the conference and present the paper. Please make sure to make early travel arrangements and take care of possible immigration requirements (e.g., visa).
The conference proceedings will be published by Springer in the Lecture Notes in Computer Science Series (LNCS). The proceedings will be published after the conference and will only include papers that were presented at the conference. Online versions of the papers will be made available at the time of the conference.
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:
March 26, 2020 April, 02 2020.
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 19 - June 4, 2020.
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.