There is a growing interest in applying mathematical theories and methods (from topology, computational geometry, differential equations, fluid dynamics, quantum statistics, etc.) to describe and to analyze scientific regularities of diverse, massive, complex, nonlinear, and fast changing data accumulated continuously around the world and in discovering and revealing the valid, insightful, and valuable knowledge that data imply. With increasingly solid mathematical foundations, various methods and techniques have been studied and developed for data mining, modeling, and processing, and knowledge representation, organization, and verification; different systems and mechanisms have been designed to perform data-intensive tasks in many application fields for classification, predication, recommendation, ranking, filtering, etc. This special issue of Mathematics in Computer Science invites submissions of original research articles on the exploration of new mathematical theories and methodologies for data modeling and analysis, and knowledge discovery and management, on the study of existing mathematical models of big data and complex knowledge, and on the development of novel solutions and strategies to enhance the performance of existing systems and mechanisms for data and knowledge processing.
Specific topics include, but are not limited to:
• Mathematical foundations and theories for data-intensive and knowledge-based systems
• Mathematical, statistical, and dynamic analysis of data and knowledge models
• Mathematical methods for big data storage, transferring, and processing
• Mathematical methods for complex knowledge representation, organization, visualization, and management
• Mathematical methods for data mining, pattern recognition, artificial intelligence, and knowledge discovery
• Algebraic, geometric, analytic, discrete, probabilistic, fuzzy, rough set, and cognitive modeling of recommendation systems, ranking systems, rating systems, expert systems, etc.
• Mathematical theories for the development of evolutionary computing, neural networks, and genetic algorithms
• Deadline for paper submission: March 31, 2013
• Notification of acceptance: August 15, 2013
• Final paper submission: October 1, 2013
• Publication of special issue: December 2013
Authors are encouraged to prepare submissions by using LaTeX with the class file mathincl.cls. Papers should be sent as PDF files to firstname.lastname@example.org. All submitted papers will be refereed according to the usual MCS refereeing process. More information can be found at: http://mine.kaust.edu.sa/Pages/CFP-MCS-SI.aspx.
Xiaoyu Chen, School of Computer Science and Engineering, Beihang University, China
Dongming Wang, Laboratoire d’Informatique de Paris 6, CNRS-UPMC, France
Xiangliang Zhang, King Abdullah University of Science and Technology, Saudi Arabia