%T Ambiguity-Directed Sampling for Qualitative Analysis of Sparse Data from Spatially-Distributed Physical Systems
%A Chris Bailey-Kellogg
%A Naren Ramakrishnan
%R Technical Report TR2001-384
%I Dartmouth College, Computer Science
%C Hanover, NH
%D January 2001
%U http://www.cs.dartmouth.edu/reports/TR2001-384.ps.Z
%X
 A number of important scientific and engineering applications, such as
 fluid dynamics simulation and aircraft design, require analysis of
 spatially-distributed data from expensive experiments and complex
 simulations.  In such data-scarce applications, it is advantageous to
 use models of given sparse data to identify promising regions for
 additional data collection.  This paper presents a principled
 mechanism for applying domain-specific knowledge to design focused
 sampling strategies.  In particular, our approach uses ambiguities
 identified in a multi-level qualitative analysis of sparse data to
 guide iterative data collection.  Two case studies demonstrate that
 this approach leads to highly effective sampling decisions that are
 also explainable in terms of problem structures and domain knowledge.