Abstract
Mining interesting patterns in data streams has attracted special attention recently. This study revealed the principles behind observations, through variation of intervention events to analyze the trends in the data streams. The main contributions includes: (a) Proposed a novel concept intervention event, and method to analyze streams under intervention. (b) Proposed the methods to evaluate the impact of intervention events. (c) Gave extensive experiments on real data to show that the newly proposed methods do prediction efficiently, and the rate of success is almost reach 92.6% recall in adaptive detection for intervention events in practical environment.
Supported by the National Science Foundation of China under Grant No. 60773169, the 11th Five Years Key Programs for Sci. &Tech. Development of China under grant No. 2006BAI05A01, and the Youth Foundation of Computer School, Sichuan University.
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Wang, Y. et al. (2009). Intervention Events Detection and Prediction in 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_45
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DOI: https://doi.org/10.1007/978-3-642-00672-2_45
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