Call for Papers
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases – ECML PKDD 2025 Porto, Portugal, September 15-19, 2025
Conference website:
ecmlpkdd.org/2025/
Call for Papers – Applied Data Science Track
The Applied Data Science Track solicits submissions that present compelling applications of Data Science and related areas (e.g., Business Analytics, Decision Support Systems, Machine Learning, Intelligent Data Analysis, Knowledge Discovery, Data Mining). The submissions should address challenging and important real-world tasks (e.g., Industry 4.0, Smart Cities, Health, Finance), thereby bridging the gap between Data Science practice and theory. The submitted papers should clearly explain the specific real-world challenges addressed (e.g., real-world domain goals, restrictions and other issues; analyzed data properties, including size and quality), the Data Science methodology used (including its evaluation procedure), and the conclusions and implications (e.g., domain impacts) that are drawn for the respective use cases. The ADS track aims to accept papers that provide practitioners with valuable insights into how to apply Data Science in real-world scenarios, showcasing their practical relevance. Alternatively, submissions may highlight novel use cases that contribute to expanding the understanding of the applicability of the Data Science methodologies in practical settings. Lastly, the track welcomes papers contributing to the overall knowledge base on the real-world application of Data Science principles. In terms of Data Science deployment level, it is expected a minimum Technology Readiness Level (TRL) of four: technology validated in laboratory environment. For example, this can include computer experiments that realistically simulate the real-world conditions (e.g., goals, restrictions) of the targeted use cases.
Authors are encouraged to comment on the practical implications and significance of their solutions, which can include deployment works or plans, and measured or estimated domain impacts in real-world scenarios or environments. Regarding methodological novelty, this track welcomes but does not require the introduction of novel Data Science techniques. Instead, the emphasis is placed on the relevance and impact of the applied solutions to real-world challenges, even if proposed systems combine previously known building blocks. Finally, to facilitate transparency and reproducibility, whenever possible, authors should disclose their computer code and/or data in a public form. In cases where there are business or industry proprietary code and/or data restrictions, acceptable solutions are the public disclosure of some portions of the code/data or their anonymized versions. Another possibility is the complementary usage of public domain Data Science libraries and datasets.
Paper Submission
Authors are invited to submit their papers using the CMT conference tool:
cmt3.research.
A list of domains and data science subject areas follow below:
Application Domain: Agriculture, Food and Earth Sciences, Arts and Humanities, Industry (4.0, 5.0, Manufacturing, …), Finance, Economy, Management or Marketing, Health, Biology, Bioinformatics or Chemistry, Social Sciences (Social Good, Psychology, History, …), Education, Engineering and Technology, Smart Cities, Transportation and Utilities (e.g., Energy), Sports, Web and Social Networks.
Data Science Approach/Method: Data Engineering (e.g., data preprocessing, integration), Descriptive (e.g., data visualization, unsupervised learning), Predictive (e.g., classification, regression, time series), Prescriptive (e.g., optimization, simulation, metaheuristics), Model Exploitation (e.g., explainability, fairness, monitoring), Machine Learning (including Deep Learning), Natural Language Processing and General Intelligence (e.g., LLM), Reinforcement Learning.
Key Dates and Deadlines
CMT Opening: 2025-02-07
Abstract Submission: 2025-03-07
Paper Submission: 2025-03-14
Author Notification: 2025-05-26
CRC Submission: 2025-06-13
Paper Format
Papers must be written in English and formatted in LaTeX, following the outline of our author kit. The kit includes a readme document, a LaTeX file template containing author instructions, and style files. 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 the formatting guidelines; further details can be found in the author kit. Up to 10 MB of additional materials (e.g., proofs, audio, images, video, data, or source code) can be uploaded with your submission. If there is an appendix, ensure it is submitted separately from your paper, which must adhere to the 16-page limit.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.
Authorship
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.
Double-blind Review
Similarly to previous years, we will apply a double-blind review-process (author identities are not known by reviewers or area chairs; reviewers do see each other’s names). All papers need to be ‘best-effort’ anonymized. 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). We strongly encourage making code and data available anonymously (e.g., in an anonymous Github repository, or Dropbox folder). The authors might have a (non-anonymous) pre-print published online, but it should not be cited in the submitted paper to preserve anonymity. Reviewers will be asked not to search for them. 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.
Conference Attendance
For each accepted paper, at least one author must register for the main conference and present the paper in person.
Proceedings
The conference proceedings will be published by Springer in the Lecture Notes in Computer Science Series (LNCS).
Reproducible Papers
Authors are strongly encouraged to adhere to the best practices of Reproducible Research, by making available data and software tools that would enable others to reproduce the results reported in their papers. We advise 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 recommended repositories and research data policy.
Ethics Considerations
Ethics is one of the most important topics to emerge in Machine Learning, Knowledge Discovery and Data Mining. We ask you to think about the ethical implications of your submission – such as those related to the collection and processing of personal data or the inference of personal information, the potential use of your work for policing or the military. You will be asked in the submission form about the ethical implications of your work which will be taken into consideration by the reviewers.
Authors Commit to Reviewing
Authors of submitted papers agree to provide the email address of at least one author who holds a PhD to be a potential PC member for ECML PKDD 2025 and may be asked to review papers for the conference if we have many more submissions than expected. This does not apply to authors who are (a) already contributing to ECML PKDD (e.g., accepted a PC/AC invite, are part of the organizing committee) or (b) not qualified to be ECML PKDD PC members (e.g., limited background in ML or DM).
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 or have submitted their papers elsewhere during the review period. 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.
Conflict 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 by that institution in the past three years, or you have extensively collaborated with the institution within the past three years. Authors should also identify other conflicts of interest, such as co-authorship in the last five years, colleagues in the same institution within the last three years, and advisor/student relations (anytime in the past).
Contact
For further information, please contact Mail:
ecml-pkdd-2025-ads-track-chairs@googlegroups.com
Carlos Ferreira
ISEP | Instituto Superior de Engenharia do Porto Rua Dr. António Bernardino de Almeida, 431 4249-015 Porto – PORTUGAL tel. +351 228 340 500 | fax +351 228 321 159 mail@isep.ipp.pt | www.isep.ipp.pt — Esta mensagem foi enviada para a rede APPIA, que engloba os associados da APPIA. Se desejar deixar de receber este tipo de mensagens, p.f. envie um email para appia [at] appia [ponto] pt
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