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A Streaming Data Prediction Method Based on Evolving Bayesian Network

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Web and Big Data (APWeb-WAIM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10367))

Abstract

In the Big Data era, large volumes of data are continuously and rapidly generated from sensor networks, social network, the Internet, etc. Learning knowledge from streaming Big Data is an important task since it can support online decision making. Prediction is one of the useful learning task but a fixed model usually does not work well because of the data distribution change over time. In this paper, we propose a streaming data prediction method based on evolving Bayesian network. The Bayesian network model is inferred based on Gaussian mixture model and EM algorithm. To support evolving model structure and parameters based on streaming data, an evolving hill-climbing algorithm is proposed which is based on incremental calculation of score metric when new data is arrived. The experimental evaluations show that this method is effective and it outperforms other popular methods for streaming data prediction.

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Notes

  1. 1.

    PeMS project, https://pems.eecs.berkeley.edu/.

References

  1. Huang, W., Song, G., Hong, H., et al.: Deep architecture for traffic flow prediction - deep belief networks with multitask learning. IEEE Trans. Intell. Transp. Syst. 15(5), 2191–2201 (2014)

    Article  Google Scholar 

  2. Zhu, S., Cheng, L., Chu, Z.: A Bayesian network model for traffic flow estimation using prior link flows. J. Southeast Univ. (English edn.) 29(3), 322–327 (2013)

    MathSciNet  MATH  Google Scholar 

  3. Angelov, P.: Autonomous Learning Systems: From Data Streams to Knowledge in Real-time. Wiley Press (2013)

    Google Scholar 

  4. Li, T.: PICKT: a solution for big data analysis. In: Proceeding of the 10th International Conference on Rough Sets and Knowledge Technology, Tianjin, China, pp. 15–25 (2015)

    Google Scholar 

  5. Yevgeniy, B., Olena, V., Iryna, P., et al.: Fast learning algorithm for deep evolving GMDH-SVM neural network in data stream mining tasks. In: Proceeding of the IEEE First International Conference on Data Stream Mining & Processing, Lviv, Ukraine, pp. 257–262 (2016)

    Google Scholar 

  6. Shtarkov, Y.M.: Universal sequential coding of single messages. Probl. Inf. Transm. 23(3), 3–17 (1987)

    MathSciNet  Google Scholar 

  7. Heckerman, D., Geiger, D., Chickering, D.M.: Learning bayesian networks: the combination of knowledge and statistical data. Mach. Learn. 20(3), 197–243 (1995)

    MATH  Google Scholar 

  8. Pascale, A., Nicoli, M.: Adaptive Bayesian network for traffic flow prediction. In: Proceeding of the Statistical Signal Processing Workshop (SSP), pp. 177–180. IEEE (2011)

    Google Scholar 

  9. Robinson, J.W., Hartemink, A.J.: Learning non-stationary dynamic Bayesian networks. J. Mach. Learn. Res. 11, 3647–3680 (2010)

    MathSciNet  MATH  Google Scholar 

  10. Yue, K., Fang, Q., Wang, X., et al.: A parallel and incremental approach for data-intensive learning of Bayesian networks. IEEE Trans. Cybern. 45(12), 2890–2904 (2015)

    Article  Google Scholar 

  11. Acharya, S., Lee, B.: Causal network construction over event streams. Inf. Sci. 261, 32–51 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  12. Bishop, C.: Pattern Recognition and Machine Learning, 2nd edn. Springer, New York (2010)

    MATH  Google Scholar 

  13. Tsamardinos, I., Brown, L.E., Aliferis, C.F.: The max-min hill-climbing Bayesian network structure learning algorithm. Mach. Learn. 65(1), 31–78 (2006)

    Article  Google Scholar 

  14. Behrisch, M., Bieker, L., Erdmann, J., et al.: Sumo - simulation of urban mobility: an overview. In: Proceeding of the Third International Conference on Advances in System Simulation (SIMUL 2011), Barcelona, Spain, 23 October 23, pp. 63–68 (2011)

    Google Scholar 

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (No.61371116).

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Authors and Affiliations

  1. College of Information Science and Electronic Engineering, Hunan University, Changsha, 410082, China

    Yongheng Wang, Guidan Chen & Zengwang Wang

Authors
  1. Yongheng Wang
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  2. Guidan Chen
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  3. Zengwang Wang
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Corresponding author

Correspondence to Yongheng Wang .

Editor information

Editors and Affiliations

  1. Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China

    Lei Chen

  2. Computer Science, Aarhus University, Aarhus N, Denmark

    Christian S. Jensen

  3. Computer Science, University of Southern California, Los Angeles, California, USA

    Cyrus Shahabi

  4. Northeastern University , Shenyang, China

    Xiaochun Yang

  5. Kent State University, Kent, Ohio, USA

    Xiang Lian

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Wang, Y., Chen, G., Wang, Z. (2017). A Streaming Data Prediction Method Based on Evolving Bayesian Network. In: Chen, L., Jensen, C., Shahabi, C., Yang, X., Lian, X. (eds) Web and Big Data. APWeb-WAIM 2017. Lecture Notes in Computer Science(), vol 10367. Springer, Cham. https://doi.org/10.1007/978-3-319-63564-4_24

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  • DOI: https://doi.org/10.1007/978-3-319-63564-4_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63563-7

  • Online ISBN: 978-3-319-63564-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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