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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Similarity Search on Uncertain Spatio-temporal Data
verfasst von : Johannes Niedermayer, Andreas Züfle, Tobias Emrich, Matthias Renz, Nikos Mamoulis, Lei Chen, Hans-Peter Kriegel
Erschienen in: Similarity Search and Applications
Verlag: Springer Berlin Heidelberg
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In this work, we address the problem of similarity search in a database of uncertain spatio-temporal objects. Each object is defined by a set of observations ((time,location)-tuples) and a Markov chain which describes the objects uncertain motion in space and time. To model similarity - which is an important building block for many applications such as identifying frequent motion patterns or trajectory clustering - we employ the well-known Longest Common Subsequence (LCSS) measure, which becomes a distribution on uncertain spatio-temporal data (ULCSS). We show how the aligned version (without time shifting) of the ULCSS can be exactly computed in PTIME, which is also verified by extensive experiments.