z-logo
Premium
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.

This content is not available in your region!

Continue researching here.

Having issues? You can contact us here