Demos

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).

Submissions

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

http://www.springer.de/comp/lncs/authors.html

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