We welcome papers that focus on the analysis of different types of complex data, such as structured, spatio-temporal and network data. We particularly welcome papers addressing applications. Finally, we would like to encourage contributions from the areas of computational scientific discovery, mining scientific data, computational creativity and discovery informatics.
DS-2015 will be collocated with ALT-2015, the 25th 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.
SUBMISSION DEADLINE EXTENDED
|Full paper submission:|
|Camera-ready papers due:||14 July 2015|
|Conference:||4-6 Oct. 2015|
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. Applications to massive, heterogeneous, continuous or imprecise data sets are of particular interests.
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. The Program Committee reserves the right to offer acceptance as Short Papers (8 pages in the Proceedings) to some submission. 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 2015 review process.
Possible topics include, but are not limited to:
- 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