Wagner Truppel

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The past decade has seen a wealth of research on time series representations, because the manipulation, storage, and indexing of large volumes of raw time series data is impractical. The vast majority of research has concentrated on representations that are calculated in batch mode and represent each value with approximately equal fidelity. However, the(More)
During the last years we have witnessed a wealth of research on approximate representations for time series. The vast majority of the proposed approaches represent each value with approximately equal fidelity, which may not be always desirable. For example, mobile devices and real time sensors have brought home the need for representations that can(More)
Time series data is perhaps the most frequently encountered type of data examined by the data mining community. Clustering is perhaps the most frequently used data mining algorithm, being useful in it’s own right as an exploratory technique, and also as a subroutine in more complex data mining algorithms such as rule discovery, indexing, summarization,(More)
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