Movatterモバイル変換


[0]ホーム

URL:


Skip to main content

Advertisement

Springer Nature Link
Log in

Adaptive Data Sampling Mechanism for Process Object

  • Conference paper
  • First Online:

Abstract

Process object is the abstraction of process. In process object, there are different type of entities and associations. The entities vary dependent on other entities. The performance and evolution of process object are affected by the association between entities. These changes could be reflected in the data collected from the process objects. These data from process object could be regard as big data stream. In the context of big data, how to find appropriate data for process object is a challenge. The data sampling should reflect the performance change of process object, and should be adaptive to the current underlying distribution of data in data stream. For finding appropriate data in big data stream to model process object, an adaptive data sampling mechanism is proposed in this paper. Experiments demonstrate the effectiveness of the proposed adaptive data sampling mechanism for process object.

This is a preview of subscription content,log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 5719
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 7149
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide -see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Similar content being viewed by others

References

  1. de Andrade Silva, J., Hruschka, E.R., Gama, J.: An evolutionary algorithm for clustering data streams with a variable number of clusters. Expert Syst. Appl.67, 228–238 (2017).https://doi.org/10.1016/j.eswa.2016.09.020

    Article  Google Scholar 

  2. Bodyanskiy, Y.V., Tyshchenko, O.K., Kopaliani, D.S.: An evolving connectionist system for data stream fuzzy clustering and its online learning. Neurocomputing262, 41–56 (2017)

    Article  Google Scholar 

  3. Dheeru, D., Karra Taniskidou, E.: UCI machine learning repository (2017).http://archive.ics.uci.edu/ml

  4. Du, T., Qu, S., Hua, Z.: A novel timing series calculation algorithm based on statistical extremum for process object. In: 9th International Conference on Computer and Automation Engineering, ICCAE 2017, Sydney, Australia, 18–21 February 2017, pp. 94–98 (2017)

    Google Scholar 

  5. Duda, P., Jaworski, M., Rutkowski, L.: On ensemble components selection in data streams scenario with reoccurring concept-drift. In: 2017 IEEE Symposium Series on Computational Intelligence, SSCI, pp. 1–7. IEEE (2017)

    Google Scholar 

  6. Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., Bouchachia, A.: A survey on concept drift adaptation. ACM Comput. Surv. (CSUR)46(4), 44 (2014)

    Article  Google Scholar 

  7. Gong, S., Zhang, Y., Yu, G.: Clustering stream data by exploring the evolution of density mountain. arXiv preprintarXiv:1710.00867 (2017)

  8. Hua, Z., Du, T., Qu, S., Mou, G.: A data stream clustering algorithm based on density and extended grid. In: Huang, D.-S., Jo, K.-H., Figueroa-García, J.C. (eds.) ICIC 2017. LNCS, vol. 10362, pp. 689–699. Springer, Cham (2017).https://doi.org/10.1007/978-3-319-63312-1_61

    Chapter  Google Scholar 

  9. Hyde, R., Angelov, P., MacKenzie, A.: Fully online clustering of evolving data streams into arbitrarily shaped clusters. Inf. Sci.382, 96–114 (2017)

    Article  Google Scholar 

  10. Liang, X., et al.: Assessing Beijing’s PM2.5 pollution: severity, weather impact, APEC and winter heating. Proc. Roy. Soc. Lond. A: Math. Phys. Eng. Sci.471(2182) (2015).https://doi.org/10.1098/rspa.2015.0257

  11. Pears, R., Sakthithasan, S., Koh, Y.S.: Detecting concept change in dynamic data streams. Mach. Learn.97(3), 259–293 (2014)

    Article MathSciNet  Google Scholar 

  12. Puschmann, D., Barnaghi, P., Tafazolli, R.: Adaptive clustering for dynamic IoT data streams. IEEE Internet Things J.4(1), 64–74 (2017)

    Google Scholar 

  13. Ross, G.J., Tasoulis, D.K., Adams, N.M.: Nonparametric monitoring of data streams for changes in location and scale. Technometrics53(4), 379–389 (2011)

    Article MathSciNet  Google Scholar 

  14. Sethi, T.S., Kantardzic, M.: Handling adversarial concept drift in streaming data. Expert Syst. Appl.97, 18–40 (2018)

    Article  Google Scholar 

  15. Sidhu, P., Bhatia, M.: Online approach to handle concept drifting data streams using diversity. Int. Arab J. Inf. Technol. (IAJIT)14(3), 293–299 (2017)

    Google Scholar 

  16. Song, Q., Guo, Q., Wang, K., Du, T., Qu, S., Zhang, Y.: A scheme for mining state association rules of process object based on big data. J. Comput. Commun.2(14), 17–24 (2014)

    Article  Google Scholar 

  17. Tennant, M., Stahl, F., Rana, O., Gomes, J.B.: Scalable real-time classification of data streams with concept drift. Future Gener. Comput. Syst.75, 187–199 (2017)

    Article  Google Scholar 

  18. Wang, L.Y., Park, C., Yeon, K., Choi, H.: Tracking concept drift using a constrained penalized regression combiner. Comput. Stat. Data Anal.108, 52–69 (2017)

    Article MathSciNet  Google Scholar 

  19. Yarlagadda, A., Jonnalagedda, M., Munaga, K.: Clustering based on correlation fractal dimension over an evolving data stream. Int. Arab J. Inf. Technol.15(1), 1–9 (2018)

    Google Scholar 

  20. Zhu, T., Du, T., Qu, S., Zhu, L.: A novel timing calculation algorithm based on statistical extremum for the time series of process object. Hans J. Data Min.6(4), 179–191 (2016)

    Article  Google Scholar 

Download references

Acknowledgment

This work was supported by the National Natural Science Foundation of China (No. 61472232), Natural Science Foundation of Shandong Province of China (No. ZR2017BF016), and the Science and Technology Program of University of Jinan (No. XKY1623).

Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Shandong Normal University, Jinan, China

    Yongzheng Lin & Hong Liu

  2. Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology, Jinan, China

    Yongzheng Lin & Hong Liu

  3. School of Information Science and Engineering, University of Jinan, Jinan, China

    Yongzheng Lin, Zhenxiang Chen, Kun Zhang & Kun Ma

Authors
  1. Yongzheng Lin

    You can also search for this author inPubMed Google Scholar

  2. Hong Liu

    You can also search for this author inPubMed Google Scholar

  3. Zhenxiang Chen

    You can also search for this author inPubMed Google Scholar

  4. Kun Zhang

    You can also search for this author inPubMed Google Scholar

  5. Kun Ma

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence toHong Liu.

Editor information

Editors and Affiliations

  1. Rutgers University, Newark, NJ, USA

    Jaideep Vaidya

  2. Guangzhou University, Guangzhou, China

    Jin Li

Rights and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Lin, Y., Liu, H., Chen, Z., Zhang, K., Ma, K. (2018). Adaptive Data Sampling Mechanism for Process Object. In: Vaidya, J., Li, J. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2018. Lecture Notes in Computer Science(), vol 11334. Springer, Cham. https://doi.org/10.1007/978-3-030-05051-1_18

Download citation

Publish with us

Access this chapter

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 5719
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 7149
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide -see info

Tax calculation will be finalised at checkout

Purchases are for personal use only


[8]ページ先頭

©2009-2025 Movatter.jp