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In the last decade unsupervised pattern discovery in time series,
i.e. the problem of finding recurrent similar subsequences in long
multivariate time series without the need of querying subsequences,
has earned more and more attention in research and industry.
Pattern discovery was already successfully applied to various areas
like seismology, medicine, robotics or music. Until now an
application to automotive time series has not been investigated.
This dissertation fills this desideratum by studying the special
characteristics of vehicle sensor logs and proposing an appropriate
approach for pattern discovery. To prove the benefit of pattern
discovery methods in automotive applications, the algorithm is
applied to construct representative driving cycles.
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