Demostrations@ECML/PKDD 2011 : ECML PKDD 2011 - Call for Demonstrations
Call For Papers
ECML PKDD 2011 - Call for Demonstrations
The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
We also invite submissions for demos of working systems based on state-of-the-art machine learning and data mining technology.
At ECML PKDD 2011 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 (4 pages) will be included in the conference proceedings, published by Springer Verlag in the "Lecture Notes in Artificial Intelligence Series."
The focus will be on innovative prototype implementation or systems that apply machine learning techniques and knowledge discovery process in practical settings. We especially encourage submissions in the fields of web, medicine, biology, neuroscience, engineering, and government.
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.
Accepted contributions: At least one of the authors must register for and attend the conference in order to present the demonstration (details 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 by May 6 to firstname.lastname@example.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 related software?
Submission deadline: 6 May 2011
Notifications: 6 June 2011
Final versions due: 12 June 2011
Demo Track Organisation
Michelangelo Ceci (Univ. Bari, Italy)
Spiros Papadimitriou (Google Research, USA)
If you have any questions, please do not hesitate to contact the Demo
Michelangelo Ceci and Spiros Papadimitriou, via email@example.com.