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.
Chair
Heinz A. Preisig
Professor:
Norwegian University of Science and Technology
Chemical Engineering
Trondheim, 7491
Norway
Email: [log in to unmask]
Co-Chair
Mano R. Maurya
Assistant Project Scientist:
Department of Bioengineering, University of California San Diego
9500 Gilman Drive, MC 0412
La Jolla, CA 92093-0412
Email: [log in to unmask]