Call for Papers

DS-2010 provides an open forum for intensive discussions and exchange of new ideas among researchers working in the area of Discovery Science. The scope of the conference includes the development and analysis of methods for automatic scientific knowledge discovery, machine learning, intelligent data analysis, theory of learning, as well as their application to knowledge discovery. Especially welcome are papers that strongly focus on the discovery aspect of the reported work. The proceedings of DS-2010 will appear in the Lecture Notes Series by Springer-Verlag.

We invite submissions of research papers addressing all aspects of discovery science. We particularly welcome contributions that discuss the application of scientific knowledge discovery and other support techniques including, but not limited to, biomedical, astronomical, space, chemistry, and physics domains.

Paper Submission

To submit your paper please log into the Submission Server

Papers may contain up to fifteen (15) pages and must be formatted according to the layout supplied by Springer-Verlag for the Lecture Notes in Computer Science series, which is available here.

Important Dates

Mentoring program submission: 16 April 2010
Abstract submission deadline: 19 May 2010
Full paper submission deadline: 24 May 2010
Notifications: 27 June 2010
Camera-ready copy: 12 July 2010
Conference: 6-8 October 2010

Mentoring Program

Based on the success of the past five years, DS-2010 is featuring once again a mentoring program. Students or groups of students that work alone are invited to submit a paper draft no later than the mentoring deadline. They will receive comments from a PC member that will help them prepare their final submission. The papers for mentoring can be submitted simply by sending the pdf version to the PC Chairs Bernhard Pfahringer (bernhard a t and Geoff Holmes (geoff a t and the Conference Chair Achim Hoffmann (achim a t

Student Award

An excellent student paper will be selected to receive the Carl Smith Award.

Submission Topics

Possible topics include, but are not limited to:
  • Logic and philosophy of scientific discovery
  • Knowledge discovery, machine learning and statistical methods
  • Ubiquitous Knowledge Discovery
  • Data Streams, Evolving Data and Models
  • Change Detection and Model Maintenance
  • Active Knowledge Discovery
  • Learning from Text and web mining
  • Information extraction from scientific literature
  • Knowledge discovery from heterogeneous, unstructured and multimedia data
  • Knowledge discovery in network and link data
  • Knowledge discovery in social networks
  • Data and knowledge visualization
  • Spatial/Temporal Data
  • Mining graphs and structured data
  • Planning to Learn
  • Knowledge Transfer
  • Computational Creativity
  • Human-machine interaction for knowledge discovery and management
  • Biomedical knowledge discovery, analysis of micro-array and gene deletion data
  • Machine Learning for High-Performance Computing, Grid and Cloud Computing
  • Applications of the above techniques to natural or social sciences
  • Other applications of the above techniques