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Computing quality scores and uncertainty for approximate pattern matching in geospatial semantic graphs
Author(s) -
Stracuzzi David J.,
Brost Randy C.,
Phillips Cynthia A.,
Robinson David G.,
Wilson Alyson G.,
Woodbridge Diane M.K.
Publication year - 2015
Publication title -
statistical analysis and data mining: the asa data science journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.381
H-Index - 33
eISSN - 1932-1872
pISSN - 1932-1864
DOI - 10.1002/sam.11294
Subject(s) - geospatial analysis , computer science , data mining , context (archaeology) , matching (statistics) , data quality , variety (cybernetics) , quality (philosophy) , information retrieval , overhead (engineering) , artificial intelligence , mathematics , geography , statistics , archaeology , economics , operating system , metric (unit) , philosophy , operations management , epistemology , cartography
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.