DS 2017 : The 20th International Conference on Discovery Science
Conference Series : Discovery Science
Call For Papers
20th International Conference on Discovery Science held in conjunction with ALT-2017
15-17 October 2017 – Kyoto, Japan http://www.iip.ist.i.kyoto-u.ac.jp/ds2017/
::: IMPORTANT DATES :::
Full paper submission: 6th June 2017
Author notification: 7th July 2017
Camera-ready papers due: 21 July 2017
::: CFP :::
The 20th International Conference on Discovery Science (DS 2017) 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 discovering scientific knowledge, coming from machine learning, data mining, intelligent data analysis, big data analysis as well as their application in various scientific domains.
We welcome papers that focus on the analysis of different types of massive and complex data, including 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-2017 will be co-located with ALT-2017 (http://www.comp.nus.edu.sg/~fstephan/alt/alt2017/), the 28th 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.
::: Invited Speakers :::
- Masashi Sugiyama (RIKEN, University of Tokyo)
- Koji Tsuda (University of Tokyo)
::: Submission Topics :::
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. 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
::: Submission Format :::
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 2017 review process.
::: Submission Mode :::
Authors can submit their papers electronically via our submission page through Easychair:
Takuya Kida, Takeaki Uno, and Tetsuji Kuboyama (PC Chairs DS)
Akihiro Yamamoto (General Chair ALT/DS)