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 scientific fields such as medicine, the natural sciences and the social sciences. To this end, DS makes use of theory, methods and techniques from various fields 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 predefined 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 scientific 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 scientific discovery
- knowledge discovery, machine learning and statistical methods
- ubiquitous 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 scientific 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
|Submission deadline||April 1, 2014|
|Author notication||June 30, 2014|
|Revised papers due||August 31, 2014|
|Final notification||September 30, 2014|
|Camera-ready due||October 31, 2014|
|Publication||Winter 2014 (planned)|
Please submit your contribution at http://ees.elsevier.com/ins/default.asp.
Make sure to select *SI:DS* as the “Article Type” in order to make sure that the paper reaches us.
Johannes Fürnkranz, Technical University of Darmstadt, Germany
Eyke Hüllermeier, University of Marburg, Germany
The PDF Version of this Call for Papers can be found here.