A probabilistic similarity query over uncertain data assigns to each uncertain database object o a probability indicating the likelihood that o meets the query predicate. In this paper, we formalize the notion of uncertain time series and introduce two novel and important types of probabilistic range queries over uncertain time series. Furthermore, we propose an original approximate representation of uncertain time series that can be used to efficiently support both new query types by upper and lower bounding the Euclidean distance. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Aßfalg, J., Kriegel, H. P., Kröger, P., & Renz, M. (2009). Probabilistic similarity search for uncertain time series. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5566 LNCS, pp. 435–443). https://doi.org/10.1007/978-3-642-02279-1_31
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