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Title: Computing quality scores and uncertainty for approximate pattern matching in geospatial semantic graphs

Geospatial semantic graphs provide a robust foundation for representing and analyzing remote sensor data. In particular, they support a variety of pattern search operations that capture the spatial and temporal relationships among the objects and events in the data. However, in the presence of large data corpora, even a carefully constructed search query may return a large number of unintended matches. This work considers the problem of calculating a quality score for each match to the query, given that the underlying data are uncertain. As a result, we present a preliminary evaluation of three methods for determining both match quality scores and associated uncertainty bounds, illustrated in the context of an example based on overhead imagery data.
Authors:
 [1] ;  [1] ;  [1] ;  [1] ;  [2] ;  [1]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  2. North Carolina State Univ., Raleigh, NC (United States)
Publication Date:
OSTI Identifier:
1236237
Report Number(s):
SAND--2015-5820J
Journal ID: ISSN 1932-1864; 618470
Grant/Contract Number:
AC04-94AL85000
Type:
Accepted Manuscript
Journal Name:
Statistical Analysis and Data Mining
Additional Journal Information:
Journal Volume: 8; Journal Issue: 5-6; Journal ID: ISSN 1932-1864
Research Org:
Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org:
USDOE National Nuclear Security Administration (NNSA)
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING uncertainty; confidence intervals; statistical models; graphical models; distance metric; image interpretation; graph search