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Abstract: 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.

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   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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