Advances in Data Analysis: Theory and Applications

The amount of data made available by the continuing advances in experimental measurement techniques has created both opportunities and major challenges towards efficient computational processing of the data sets. Extracting useful information (i.e., knowledge) from these large data sets requires an integral approach that couples the design of experiments or measurement techniques with the algorithms or applications required for the data processing and analysis. New tools for representing, extracting, and using the knowledge available in data sets are expected to emerge to perform tasks in data-rich, analysis-rich situations rather than being data-poor, analysis-rich, which has been the traditional scenario until recently. Contributions are sought that propose new modeling approaches and computational algorithms for addressing issues related to data analysis, management and data-based decision making. Suggested topics include, but are not limited to: * Model based experimental design and use of new data to refine models * Analysis of complex systems including material and biological * Use of data analysis for design, optimization, and control * Interplay of first principles and data driven models in knowledge extraction. The following topics are also of interest to this session: * Distributed decision making in organizations * Decentralized information processing * Financial modeling & investment planning * Information management across the WWW. Applications with industrial relevance are strongly encouraged.