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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.
Bibliographic citation for this report: [plain text] [BIB] [BibTeX] [Refer]
Or copy and paste:
Chris Bailey-Kellogg and Naren Ramakrishnan, "Ambiguity-Directed Sampling for Qualitative Analysis of Sparse Data from Spatially-Distributed Physical Systems." Dartmouth Computer Science Technical Report TR2001-384, January 2001.
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