Call for Papers
Special Issue on Discovery Science
in Information Sciences


Scope and Background

The Information Sciences journal (Elsevier) is soliciting submissions on Discovery Science (DS), a research discipline that is concerned with the development, analysis and application of computational methods and tools to support the automatic or semi-automatic discovery of knowledge in scientic elds such as medicine, the natural sciences and the social sciences. To this end, DS makes use of theory, methods and techniques from various elds of computer science and applied mathematics, notably machine learning and data mining, intelligent data analysis, statistics, optimizations, algorithms and complexity, as well as databases and information systems.
Contrary to conventional statistical analysis, which makes use of data to verify the validity of predened hypotheses, discovery sciences is more geared toward the discovery of the hypotheses themselves. Thus, it puts particular emphasis on increasing our understanding of the process of hypothesis formation, as opposed to the areas of machine learning and data mining, which focus on the hypotheses and their predictive quality. In terms of applications, discovery science puts special emphasis on the analysis of scientic data originating from various disciplines, as opposed to the strong commercial focus of many data mining conferences and journals.


Topics of Interest

Topics of interest include, but are not limited to:

logic and philosophy of scientic discovery
knowledge discovery, machine learning and statistical methods
biquitous knowledge discovery
knowledge discovery from heterogeneous, unstructured and multimedia data
knowledge discovery in network and link data
knowledge discovery in social networks
active learning and knowledge discovery
text and web mining
declarative approaches for data mining
information extraction from scientic literature
data streams, evolving data and models
data and knowledge visualization
spatial/temporal data analysis
mining graphs and structured data
knowledge transfer and transfer learning
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 in the natural or social sciences



Schedule
Submission deadline March 1, 2014
Author notication May 30, 2014
Revised papers due July 31, 2014
Final notication August 30, 2014
Camera-ready due September 30, 2014
Publication Winter 2014 (planned)



Guest Editors

Johannes Fürnkranz, Technical University of Darmstadt, Germany
Eyke Hüllermeier, University of Marburg, Germany