Computing quality scores and uncertainty for approximate pattern matching in geospatial semantic graphs
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- North Carolina State Univ., Raleigh, NC (United States)
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.
- Research Organization:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA)
- Grant/Contract Number:
- AC04-94AL85000
- OSTI ID:
- 1236237
- Report Number(s):
- SAND-2015-5820J; 618470
- Journal Information:
- Statistical Analysis and Data Mining, Vol. 8, Issue 5-6; ISSN 1932-1864
- Country of Publication:
- United States
- Language:
- English
Web of Science
Similar Records
Geospatial-Temporal Semantic Graphs for Automated Wide-Area Search
Encoding and analyzing aerial imagery using geospatial semantic graphs