Abstract
In this paper, we present a method to trace evolution of trend over multiple data streams and detect the abnormal ones. First of all, a definition of trend for single data stream is provided, the advantage of our definition lies in its low time and space cost. Second, we improve a SVD-based method in order to select a pair of optimal initial parameters, then a novel chessboard named sketch is also illustrated aim at adjusting the parameters dynamically. Then, utilizing the skewness of trend distribution, an anomaly detection strategy is briefly introduced. Finally, we implement experiment on a variety of real data sets to illustrate effectiveness and efficiency of our approach.
This work is supported by Shanghai Leading Academic Discipline Project (Project Number: B412).
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Zhang, C., Weng, N., Chang, J., Zhou, A. (2009). Detecting Abnormal Trend Evolution over Multiple Data Streams. In: Li, Q., Feng, L., Pei, J., Wang, S.X., Zhou, X., Zhu, QM. (eds) Advances in Data and Web Management. APWeb WAIM 2009 2009. Lecture Notes in Computer Science, vol 5446. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00672-2_26
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DOI: https://doi.org/10.1007/978-3-642-00672-2_26
Publisher Name: Springer, Berlin, Heidelberg
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