LWDA Workshop on Knowledge Discovery, Data Mining and Machine Learning

Submission deadline June 30 July 14, 2022 – for long and short papers.

KDML is a workshop series that aims at bringing together the German Machine Learning and Data Mining community. The KDML 2022 Workshop is co-located with the annual LWDA 2022 – Learning, Knowledge, Data, and Analysis – conference and will take place from October 5 to October 7, 2022, hosted at the University of Hildesheim. Currently, the conference is planned to be held in person.

Call for Papers

We invite submissions on all aspects of data mining, knowledge discovery, and machine learning. In addition to original research, we also invite resubmissions of recently published articles at major conference venues related to KDML. Moreover, KDML explicitly invites student submissions.

This year, we particularly encourage submissions addressing machine learning and data mining on graphs or networks as our focus topic of KDML 2022. We encourage submissions on theory, methods, and applications. Networks and graphs arise as natural representations of data in many application areas, but suitable re-representation is often required to properly learn. Understanding the methodological options for different use cases as well as limitations of our current tools and insights might help us improve on the state of the art.

Topics of interest include but are not limited to

  1. Foundations, algorithms, models, and theory of machine learning and data mining
  2. Supervised, semi-supervised, and unsupervised learning
  3. Machine learning on networks and graphs
  4. Deep learning and representation learning
  5. Rule-based learning and pattern mining
  6. Reinforcement learning
  7. Fairness, transparency and formal guarantees in machine learning
  8. Explainability and Interpretability in machine learning and knowledge discovery
  9. Temporal, spatial & spatio-temporal data analytics
  10. Online learning and machine learning in data streams
  11. Text mining, mining unstructured and semi-structured data
  12. Parallel and distributed data analytics
  13. Interactive and visual analytics
  14. Applications of data mining and machine learning in all domains including natural-, life-,
    and social sciences, health, financial, environment, engineering, and humanities
  15. Open source frameworks and tools for data mining and machine learning

Types of Submissions

We solicit new contributions (up to 12 pages, peer-reviewed and to be published by LWDA). Shorter contributions (4 pages) are also possible. We welcome submissions in English and German, however English is preferred. All papers must be formatted according to the current CEUR template provided on the LWDA homepage. All contributions must be submitted via EasyChair using the link:

only PDF is permitted. Please select the track „FG-KDML’’ for your submission.

We further welcome submissions of works accepted recently at top-tier international venues related to KDML (e.g., ECMLPKDD, ICML, NIPS, KDD, ICLR, IJCAI, AAAI, ICDM, SDM, etc.). These will not be reviewed but selected by the PC chairs. They will not be included in the LWDA proceedings. Please clearly indicate in the abstract of your EasyChair submission that your paper is a resubmission.

Evaluation Process and Publication of Unpublished Submissions

Each submission will be reviewed by at least two independent reviewers. The conference proceedings will be published as CEUR Workshop Proceedings and will be indexed by DBLP.

Participation and Presentations

All workshop participants have to register for the LWDA 2022 conference. Papers will be accepted for either long (20 min) or short (10 min) presentations; authors are expected to also prepare a poster presentation of their work.


  • Submission deadline: 30.6.2022 14.07.2022
  • Notification of acceptance: 30.8.2022 06.09.2022
  • Camera-ready copy: 22.9.2022
  • LWDA 2022 Conference: 5.10. – 7.10.2022

Dr. Pascal Welke, Rheinische Friedrich-Wilhelms-Universität Bonn
Felix Stamm, Rheinisch-Westfälische Technische Hochschule Aachen