Supervised Anomaly Detection in Uncertain Pseudoperiodic Data Streams
  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 paper, we present a novel framework that
supports anomaly detection in uncertain data streams. The proposed framework
adopts an efficient uncertainty pre-processing procedure to identify and
eliminate uncertainties in data streams. Based on the corrected 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 a
number of real datasets.
