The power of sampling in knowledge discovery
PODS '94 Proceedings of the thirteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Using particles to sample and control implicit surfaces
SIGGRAPH '94 Proceedings of the 21st annual conference on Computer graphics and interactive techniques
Qualitative and quantitative simulation: bridging the gap
Artificial Intelligence
Influence-based model decomposition
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Intelligent Aids for Parallel Experiment Planning and Macromolecular Crystallization
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
STA: Spatio-Temporal Aggregation with Applications to Analysis of Diffusion-Reaction Phenomena
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Normalized Cuts and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Learning evaluation functions to improve optimization by local search
The Journal of Machine Learning Research
Spatial aggregation: theory and applications
Journal of Artificial Intelligence Research
Spatial aggregation: language and applications
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Sampling Strategies for Mining in Data-Scarce Domains
Computing in Science and Engineering
Spatial aggregation for qualitative assessment of scientific computations
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Gaussian process models of spatial aggregation algorithms
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
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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.