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Mining Correlations Between Multi-streams Based on Haar Wavelet

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Book cover Advances in Computer Science – ASIAN 2005. Data Management on the Web (ASIAN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3818))

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Abstract

Mining correlation between multi-streams is a significant task. The main contributions of this paper included: (1) Proposes the equivalence model and equivalence theorems to computing correlation coefficient. (2) Designs anti-noise algorithm with sliding windows to compute correlation measure. (3) Gives extensive experiments on real data and shows that new algorithm works very well on the streams with noise in the environment of short size windows.

This work was supported by Grant from National Science Foundation of China (T60473071), Specialized Research Fund for Doctoral Program by the Ministry of Education (SRFDP20020610007), CHEN Anlong, YUAN Changan, PENG Jing, HU Jianjun are Ph. D Candidates at DB&KE Lab, Sichuan University. And TANG Changjie is the associate author.

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References

  1. Zhu, Y., Shasha, D.: StatStream: Statistical Monitoring of Thousands of Data Streams in Real Time. In: VLDB, August 2002, pp. 358–369 (2002)

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  2. Dula, S., Kim, C., Shim, K.: XWAVE: Optimal and Approximate Extended Wavelets for Streaming Data. In: VLDB, Toronto, Canada (2004)

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  3. Anlong, C., Changjie, T., Changan, Y., Jing, P., Jianjun, H.: The Full Version of This Paper. Mining Correlations between Multi-Streams Based on Haar Wavelet, SciencePaperOnline No20050911, http://www.paper.edu.cn

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© 2005 Springer-Verlag Berlin Heidelberg

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Chen, A., Tang, C., Yuan, C., Peng, J., Hu, J. (2005). Mining Correlations Between Multi-streams Based on Haar Wavelet. In: Grumbach, S., Sui, L., Vianu, V. (eds) Advances in Computer Science – ASIAN 2005. Data Management on the Web. ASIAN 2005. Lecture Notes in Computer Science, vol 3818. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11596370_32

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  • DOI: https://doi.org/10.1007/11596370_32

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-30767-9

  • Online ISBN: 978-3-540-32249-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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