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

Statistical Analysis and Data Mining

Stracuzzi, David J.; Brost, Randolph B.; Phillips, Cynthia A.; Robinson, David G.; Wilson, Alyson G.; Woodbridge, Diane W.

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. 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.

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