BIB-VERSION:: CS-TR-v2.0
ID:: ncstrl.dartmouthcs//TR2001-384
ENTRY:: January 11, 2001
ORGANIZATION:: Dartmouth College, Computer Science
TITLE:: Ambiguity-Directed Sampling for Qualitative Analysis of Sparse Data from Spatially-Distributed Physical Systems
TYPE:: Technical Report (paper)
REVISION:: 1
AUTHOR:: Bailey-Kellogg, Chris
AUTHOR:: Ramakrishnan, Naren
DATE:: January 2001
RETRIEVAL:: For a paper copy, email <reports@cs.dartmouth.edu>
RETRIEVAL:: For a paper copy, write to
       Technical Report Librarian
       Department of Computer Science
       Dartmouth College
       6211 Sudikoff Laboratory
       Hanover, NH 03755-3510
       USA
RETRIEVAL:: Compressed Postscript at http://www.cs.dartmouth.edu/reports/TR2001-384.ps.Z
RETRIEVAL:: PDF at http://www.cs.dartmouth.edu/reports/TR2001-384.pdf
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.
END:: ncstrl.dartmouthcs//TR2001-384