2009 | OriginalPaper | Buchkapitel
Probabilistic Similarity Search for Uncertain Time Series
verfasst von : Johannes Aßfalg, Hans-Peter Kriegel, Peer Kröger, Matthias Renz
Erschienen in: Scientific and Statistical Database Management
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
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.