Abstract
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.
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Assfalg, J., Kriegel, H.P., Kröger, P., Renz, M.: Probabilistic Similarity Search for Uncertain Time Series. Tech. Rep. (2009), http://www.dbs.ifi.lmu.de/~renz/technicalReports/uncertainTimeSeries.pdf
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Aßfalg, J., Kriegel, HP., Kröger, P., Renz, M. (2009). Probabilistic Similarity Search for Uncertain Time Series. In: Winslett, M. (eds) Scientific and Statistical Database Management. SSDBM 2009. Lecture Notes in Computer Science, vol 5566. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02279-1_31
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DOI: https://doi.org/10.1007/978-3-642-02279-1_31
Publisher Name: Springer, Berlin, Heidelberg
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