Call for papers
The 15th International Conference on Discovery Science (DS-2012) will be held in Lyon, France, on 29-31 October 2012.
DS-2012 will be collocated with ALT-2012, the 23rd International Conference on Algorithmic Learning Theory. The two conferences will be held in parallel, and will share their invited talks.
Traditionally the proceedings of DS series appear in the Lecture Notes in Artificial Intelligence Series by Springer-Verlag. In addition a special issue on Discovery Science is planned in a prestigious journal.
DS 2012 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. Very welcome are papers that focus on dynamic and evolving data, models and structures.
Important Dates are:
|Full paper submission:
|8th July, 2012
|Camera-ready papers due:
|17th July, 2012
|29-31 Oct. 2012
We invite submissions of research papers addressing all aspects of discovery science. We particularly welcome contributions that discuss the application of data analysis, data mining and other support techniques for scientific discovery including, but not limited to, biomedical, astronomical and other physics domains.
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. Submitted papers may not have appeared in or be under consideration for another workshop, conference or a journal, nor may they be under review or submitted to another forum during the DS 2012 review process.
An excellent student paper will be selected to receive the Carl Smith Award. The award carries a scholarship prize of 555 euros.
Submission TopicsPossible 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
Co-chairs of the PC