- Yongzheng Lin16,17,18,
- Hong Liu16,17,
- Zhenxiang Chen18,
- Kun Zhang18 &
- …
- Kun Ma ORCID:orcid.org/0000-0002-0135-542318
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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.
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References
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
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)
Dheeru, D., Karra Taniskidou, E.: UCI machine learning repository (2017).http://archive.ics.uci.edu/ml
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)
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)
Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., Bouchachia, A.: A survey on concept drift adaptation. ACM Comput. Surv. (CSUR)46(4), 44 (2014)
Gong, S., Zhang, Y., Yu, G.: Clustering stream data by exploring the evolution of density mountain. arXiv preprintarXiv:1710.00867 (2017)
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
Hyde, R., Angelov, P., MacKenzie, A.: Fully online clustering of evolving data streams into arbitrarily shaped clusters. Inf. Sci.382, 96–114 (2017)
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
Pears, R., Sakthithasan, S., Koh, Y.S.: Detecting concept change in dynamic data streams. Mach. Learn.97(3), 259–293 (2014)
Puschmann, D., Barnaghi, P., Tafazolli, R.: Adaptive clustering for dynamic IoT data streams. IEEE Internet Things J.4(1), 64–74 (2017)
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)
Sethi, T.S., Kantardzic, M.: Handling adversarial concept drift in streaming data. Expert Syst. Appl.97, 18–40 (2018)
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)
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)
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)
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)
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)
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)
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).
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Authors and Affiliations
School of Information Science and Engineering, Shandong Normal University, Jinan, China
Yongzheng Lin & Hong Liu
Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology, Jinan, China
Yongzheng Lin & Hong Liu
School of Information Science and Engineering, University of Jinan, Jinan, China
Yongzheng Lin, Zhenxiang Chen, Kun Zhang & Kun Ma
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Correspondence toHong Liu.
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Rutgers University, Newark, NJ, USA
Jaideep Vaidya
Guangzhou University, Guangzhou, China
Jin Li
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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
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