We invite submissions for demos of working systems based on state-of-the-art machine-learning and data-mining technology.

At ECML PKDD 2010 a special demonstration session will be held, an exciting and highly interactive way to showcase the state of the art in machine learning and knowledge discovery software. Accepted demonstration papers will be published in the conference proceedings (4 pages).

The focus will be on innovative prototype implementations, systems, and technologies in machine learning and data analysis, concerned with (non-exhaustive list):

  • Information retrieval
  • Text and language analysis/processing
  • Media (image/video/audio/...) analysis/processing
  • Visual data mining
  • Mobile applications
  • Sensor networks
  • Robotics
  • e-Science
  • Industrial applications
  • Business intelligence
  • Multimodal HCI, including Brain Computer Interfaces

Submissions will be judged by a committee of technical experts with expertise in machine learning, data mining, and software engineering. Selection criteria include the relevance of the contribution, its interest and usefulness for attendees, and its
technical difficulty. Special attention will be devoted to open-source software, although not a requirement for submission.

Accepted contributions: At least one of the authors must register for and attend the conference in order to present the demonstration (precisions about demonstrations to be given with the acceptance notification).




Each demonstration should be accompanied by a short paper of at most 4 pages (including figures and screenshots if needed). The paper must be in English and must be formatted according to the Springer-Verlag Lecture Notes in Artificial Intelligence guidelines. Authors instructions and style files can be downloaded at

Please submit your paper electronically until May 14 to demos@ecmlpkdd2010.org

In this accompanying paper, please try to answer the following questions: What makes your piece of software unique and special? What are the innovative aspects or in what way/area does it represent the state of the art? For whom is it most interesting/useful? (an ML or KDD researcher, a graduate or undergraduate student in these areas, an industrial practitioner etc.) If there are similar/related pieces of software: What are the advantages and disadvantages compared to these related software?


Important Dates

Submission deadline: May 14
Notifications: June 7
Final versions due: June 21

Program Committee

  • Ulf Brefeld, Yahoo! Research, Barcelona (co-chair)
  • Xavier Carreras, UPC, Barcelona (co-chair)
  • Sebastian Blohm, Microsoft, Munich
  • Christian Borgelt, European Centre for Soft Computing, Mieres
  • Massimiliano Ciaramita, Google Zurich
  • Mayur Datar, Stanford University
  • Tijl de Bie, University Of Bristol
  • David Demirdjian, Vecna Technologies, Inc.
  • Maria Fuentes, UPC, Barcelona
  • Andreas Hotho, University of Wuerzburg
  • Felix Jungermann, University of Dortmund
  • Nattiya Kanhabua, Norwegian University of Science and Technology
  • Hady Lauw, Institute for Infocomm Research, Singapore
  • Ingo Mierswa, Rapid-I, Dortmund
  • Nuria Oliver Ramirez, Telefonica R&D, Barcelona
  • Mykola Pechenizkiy, Eindhoven University of Technology
  • Konrad Rieck, TU Berlin
  • Soeren Sonnenburg, TU Berlin
  • Roberto Trasarti, University of Pisa
  • Panayiotis Tsaparas, Microsoft Research, Mountain View
  • Vasin Punyakanok, BBN Technologies


In case you have any question, please do not hesitate to contact the Demo Chairs Ulf Brefeld and Xavier Carreras via demos@ecmlpkdd2010.org

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