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• W2265638863 abstract "Uncertain data streams have been widely generated in many Web applications. The uncertainty in data streams makes anomaly detection from sensor data streams far more challenging. In this article, we present a novel framework that supports anomaly detection in uncertain data streams. The proposed framework adopts the wavelet soft-thresholding method to remove the noises or errors in data streams. Based on the refined data streams, we develop effective period pattern recognition and feature extraction techniques to improve the computational efficiency. We use classification methods for anomaly detection in the corrected data stream. We also empirically show that the proposed approach shows a high accuracy of anomaly detection on several real datasets." @default.
• W2265638863 created "2016-06-24" @default.
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• W2265638863 date "2016-01-22" @default.
• W2265638863 modified "2023-10-16" @default.
• W2265638863 title "Supervised Anomaly Detection in Uncertain Pseudoperiodic Data Streams" @default.
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• W2265638863 doi "https://doi.org/10.1145/2806890" @default.
• W2265638863 hasPublicationYear "2016" @default.
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