Ambiguity-Directed Sampling for Qualitative Analysis of Sparse Data from Spatially-Distributed Physical Systems

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

Ambiguity-Directed Sampling for Qualitative Analysis of Sparse Data from Spatially Distributed Physical Systems

A number of important scientific and engineering applications, such as fluid dynamics simulation and aircraft design, require analysis of spatiallydistributed 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.

Department of Computer Science Activity 1998-2004

This report summarizes much of the research and teaching activity of the Department of Computer Science at Dartmouth College between late 1998 and late 2004. The material for this report was collected as part of the final report for NSF Institutional Infrastructure award EIA-9802068, which funded equipment and technical staff during that six-year period. This equipment and staff supported essentially all of the department\u27s research activity during that period