Similarity Search on Uncertain Spatio-temporal Data [chapter]

Johannes Niedermayer, Andreas Züfle, Tobias Emrich, Matthias Renz, Nikos Mamoulis, Lei Chen, Hans-Peter Kriegel
2013 Lecture Notes in Computer Science  
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
more » ... 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.
doi:10.1007/978-3-642-41062-8_5 fatcat:adhft75durdvfkzmugv6atc3lu