Skip to main content
SpringerLink
Log in

CaFtR: A Fuzzy Complex Event Processing Method

  • Published:
International Journal of Fuzzy Systems Aims and scope Submit manuscript

Abstract

Fuzzy complex event processing-based decision-making systems have received considerable research interests recently. In particular, a well-managed operator distribution is required for improving the performance of the fuzzy complex event processing-based decision-making systems. However, the intrinsic uncertainty in dynamic input events increases the difficulties of operator distribution problem. To address these issues, a cost-aware, fault-tolerant and reliable strategy, called CaFtR is proposed for operator scheduling on fuzzy complex event processing systems based on the Technique for Order Preferences by Similarity to an Ideal Solution. The proposed CaFtR method adequately makes use of network resources to achieve continuous and highly available complex event processing regardless of dynamic operator migrations under fuzzy environment. Finally, a case study is provided to illustrate the efficiency of the proposed method, and the utility of our work is demonstrated through an application on the StreamBase system.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Cao, Z., Chuang, C.-H., King, J.-K., Lin, C.-T.: Multi-channel EEG recordings during a sustained-attention driving task. Sci. Data 6 (2019). https://doi.org/10.1038/s41597-019-0027-4

  2. Xiao, F.: CED: a distance for complex mass functions. IEEE Trans. Neural Netw. Learn. Syst. 32(4), 1525–1535 (2021)

    Article  MathSciNet  Google Scholar 

  3. Deng, X., Jiang, W., Wang, Z.: An information source selection model based on evolutionary game theory. Appl. Math. Comput. 385, 125362 (2020)

    MathSciNet  MATH  Google Scholar 

  4. Xiao, F.: On the maximum entropy negation of a complex-valued distribution. IEEE Trans. Fuzzy Syst. (2020). https://doi.org/10.1109/TFUZZ.2020.3016723

  5. Roldán, J., Boubeta-Puig, J., Martínez, J.L., Ortiz, G.: Integrating complex event processing and machine learning: an intelligent architecture for detecting IoT security attacks. Expert Syst. Appl. 149, 113251 (2020)

    Article  Google Scholar 

  6. Nardelli, M., Cardellini, V., Grassi, V., Presti, F.L.: Efficient operator placement for distributed data stream processing applications. IEEE Trans. Parallel Distrib. Syst. 30(8), 1753–1767 (2019)

    Article  Google Scholar 

  7. Xiao, F., Aritsugi, M., Wang, Q., Zhang, R.: Efficient processing of multiple nested event pattern queries over multi-dimensional event streams based on a triaxial hierarchical model. Artif. Intell. Med. 72, 56–71 (2016)

    Article  Google Scholar 

  8. Deng, Y.: Uncertainty measure in evidence theory. Sci. China Inf. Sci. 63(11), 210201 (2020)

    Article  MathSciNet  Google Scholar 

  9. Jiang, W., Huang, K., Geng, J., Deng, X.: Multi-scale metric learning for few-shot learning. IEEE Trans. Circ. Syst. Video Technol. (2020). https://doi.org/10.1109/TCSVT.2020.2995754

  10. Meng, D., Xie, T., Wu, P., Zhu, S.-P., Hu, Z., Li, Y.: Uncertainty-based design and optimization using first order saddle point approximation method for multidisciplinary engineering systems. ASCE-ASME J. Risk Uncertain. Eng. Syst. A 6(3), 04020028 (2020)

    Article  Google Scholar 

  11. Zhou, M., Liu, X.-B., Chen, Y.-W., Qian, X.-F., Yang, J.-B., Wu, J.: Assignment of attribute weights with belief distributions for MADM under uncertainties. Knowl. Based Syst. 189, 105110 (2020)

    Article  Google Scholar 

  12. Xiao, F.: Generalization of Dempster–Shafer theory: a complex mass function. Appl. Intell. 50(10), 3266–3275 (2019)

    Article  Google Scholar 

  13. Xiao, F.: Generalized belief function in complex evidence theory. J. Intell. Fuzzy Syst. 38(4), 3665–3673 (2020)

    Article  Google Scholar 

  14. Deng, J., Deng, Y.: Information volume of fuzzy membership function. Int. J. Comput. Commun. Control 16(1), 4106 (2021). https://doi.org/10.15837/ijccc.2021.1.4106

    Article  Google Scholar 

  15. Fei, L., Feng, Y.: Modeling interactive multiattribute decision-making via probabilistic linguistic term set extended by Dempster-Shafer theory. Int. J. Fuzzy Syst. 1–15 (2021)

  16. Liao, H., Ren, Z., Fang, R.: A Deng-entropy-based evidential reasoning approach for multi-expert multi-criterion decision-making with uncertainty. Int. J. Comput. Intell. Syst. 13(1), 1281–1294 (2020)

    Article  Google Scholar 

  17. Song, Y., Zhu, J., Lei, L., Wang, X.: A self-adaptive combination method for temporal evidence based on negotiation strategy. Sci. China Inf. Sci. 63(11), 1–13 (2020)

    Article  Google Scholar 

  18. Tang, M., Liao, H., Herrera-Viedma, E., Chen, C.P., Pedrycz, W.: A dynamic adaptive subgroup-to-subgroup compatibility-based conflict detection and resolution model for multicriteria large-scale group decision making. IEEE Trans. Cybern. (2020). https://doi.org/10.1109/TCYB.2020.2974924

  19. Fei, L., Feng, Y., Liu, L.: On Pythagorean fuzzy decision making using soft likelihood functions. Int. J. Intell. Syst. 34(12), 3317–3335 (2019)

    Article  Google Scholar 

  20. Xu, X., Zheng, J., Yang, J.-B., Xu, D.-L., Chen, Y.-W.: Data classification using evidence reasoning rule. Knowl. Based Syst. 116, 144–151 (2017)

    Article  Google Scholar 

  21. Liu, Z., Pan, Q., Dezert, J., Han, J.-W., He, Y.: Classifier fusion with contextual reliability evaluation. IEEE Trans. Cybern. 48(5), 1605–1618 (2018)

    Article  Google Scholar 

  22. Ma, J., Ma, Y., Li, C.: Infrared and visible image fusion methods and applications: a survey. Inf. Fusion 45, 153–178 (2019)

    Article  Google Scholar 

  23. Xiao, F.: GIQ: A generalized intelligent quality-based approach for fusing multi-source information. IEEE Trans. Fuzzy Syst. (2020). https://doi.org/10.1109/TFUZZ.2020.2991296

  24. Pan, Y., Zhang, L., Wu, X., Skibniewski, M.J.: Multi-classifier information fusion in risk analysis. Inf. Fusion 60, 121–136 (2020)

    Article  Google Scholar 

  25. Xiao, F.: Evidence combination based on prospect theory for multi-sensor data fusion. ISA Trans. 106, 253–261 (2020)

    Article  Google Scholar 

  26. Yager, R.R.: Inferring the value of a variable using measure based information of a related variable. Eng. Appl. Artif. Intell. 101, 104201 (2021)

    Article  Google Scholar 

  27. Deng, Y.: Information volume of mass function. Int. J. Comput. Commun. Control 15(6), 3983 (2020)

    Article  Google Scholar 

  28. Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)

    Article  Google Scholar 

  29. Hwang, C.-L., Yoon, K.: Methods for multiple attribute decision making. In: Multiple Attribute Decision Making, pp. 58–191. Springer, Berlin (1981)

  30. Chen, C.-T.: Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets Syst. 114(1), 1–9 (2000)

    Article  Google Scholar 

  31. http://www.streambase.com/ (2018)

  32. Fujita, H., Gaeta, A., Loia, V., Orciuoli, F.: Hypotheses analysis and assessment in counter-terrorism activities: a method based on OWA and fuzzy probabilistic rough sets. IEEE Trans. Fuzzy Syst. 28, 831–845 (2019)

    Article  Google Scholar 

  33. Li, Y.-F., Huang, H.-Z., Mi, J., Peng, W., Han, X.: Reliability analysis of multi-state systems with common cause failures based on Bayesian network and fuzzy probability. Ann. Oper. Res. 1–15 (2019)

  34. Pan, L., Gao, X., Deng, Y., Cheong, K.H.: The constrained Pythagorean fuzzy sets and its similarity measure. IEEE Trans. Fuzzy Syst. (2021). https://doi.org/10.1109/TFUZZ.2021.3052559

  35. Gao, X., Pan, L., Deng, Y.: Quantum pythagorean fuzzy evidence theory (QPFET): a negation of quantum mass function view. IEEE Trans. Fuzzy Syst. (2021). https://doi.org/10.1109/TFUZZ.2021.3057993

  36. Feng, F., Xu, Z., Fujita, H., Liang, M.: Enhancing PROMETHEE method with intuitionistic fuzzy soft sets. Int. J. Intell. Syst. 35, 1071–1104 (2020)

    Article  Google Scholar 

  37. Feng, F., Cho, J., Pedrycz, W., Fujita, H., Herawan, T.: Soft set based association rule mining. Knowl. Based Syst. 111, 268–282 (2016)

    Article  Google Scholar 

  38. Yang, J., Li, S., Xu, Z., Liu, H., Yao, W.: An understandable way to extend the ordinary linear order on real numbers to a linear order on interval numbers. IEEE Trans. Fuzzy Syst. (2020). https://doi.org/10.1109/TFUZZ.2020.3006557

  39. Pan, Y., Zhang, L., Li, Z., Ding, L.: Improved fuzzy Bayesian network-based risk analysis with interval-valued fuzzy sets and D-S evidence theory. IEEE Trans. Fuzzy Syst. (2019). https://doi.org/10.1109/TFUZZ.2019.2929024

  40. Tian, Y., Mi, X., Liu, L., Kang, B.: A new soft likelihood function based on D numbers in handling uncertain information. Int. J. Fuzzy Syst. 22(7), 2333–2349 (2020)

    Article  Google Scholar 

  41. Tian, Y., Liu, L., Mi, X., Kang, B.: ZSLF: A new soft likelihood function based on Z-numbers and its application in expert decision system. IEEE Trans. Fuzzy Syst. (2020). https://doi.org/10.1109/TFUZZ.2020.2997328

  42. Jiang, W., Cao, Y., Deng, X.: A novel Z-network model based on Bayesian network and Z-number. IEEE Trans. Fuzzy Syst. 28(8), 1585–1599 (2020)

    Article  Google Scholar 

  43. Liu, Q., Tian, Y., Kang, B.: Derive knowledge of Z-number from the perspective of Dempster–Shafer evidence theory. Eng. Appl. Artif. Intell. 85, 754–764 (2019)

    Article  Google Scholar 

  44. Liao, H., Mi, X., Xu, Z.: A survey of decision-making methods with probabilistic linguistic information: bibliometrics, preliminaries, methodologies, applications and future directions. Fuzzy Optim. Decis. Making 19, 81–134 (2020)

    Article  MathSciNet  Google Scholar 

  45. Liu, P., Zhang, X., Pedrycz, W.: A consensus model for hesitant fuzzy linguistic group decision-making in the framework of Dempster–Shafer evidence theory. Knowl. Based Syst. 212, 106559 (2020)

    Article  Google Scholar 

  46. Fang, R., Liao, H., Yang, J.-B., Xu, D.-L.: Generalised probabilistic linguistic evidential reasoning approach for multi-criteria decision-making under uncertainty. J. Oper. Res. Soc. 2, 1–15 (2020)

    Google Scholar 

  47. Gou, X., Liao, H., Xu, Z., Min, R., Herrera, F.: Group decision making with double hierarchy hesitant fuzzy linguistic preference relations: consistency based measures, index and repairing algorithms and decision model. Inf. Sci. 489, 93–112 (2019)

    Article  MathSciNet  Google Scholar 

  48. Fu, C., Chang, W., Yang, S.: Multiple criteria group decision making based on group satisfaction. Inf. Sci. 518, 309–329 (2020)

    Article  MathSciNet  Google Scholar 

  49. Xiao, F.: A distance measure for intuitionistic fuzzy sets and its application to pattern classification problems. IEEE Trans. Syst. Man. Cybern. (2019) https://doi.org/10.1109/TSMC.2019.2958635

  50. Liu, Z., Liu, Y., Dezert, J., Cuzzolin, F.: Evidence combination based on credal belief redistribution for pattern classification. IEEE Trans. Fuzzy Syst. 28(4), 618–631 (2020)

    Article  Google Scholar 

  51. Wen, T., Cheong, K.H.: The fractal dimension of complex networks: a review. Inf. Fusion 73, 87–102 (2021)

    Article  Google Scholar 

  52. Xue, Y., Deng, Y., Garg, H.: Uncertain database retrieval with measure-based belief function attribute values under intuitionistic fuzzy set. Inf. Sci. (2020)

  53. Xiao, F., Teruaki, K.M.: Aritsugi, economical and fault-tolerant load balancing in distributed stream processing systems. IEICE Trans. Inf. Syst. 95(4), 1062–1073 (2012)

    Article  Google Scholar 

  54. Kim, J.-K., Lee-Kwang, H., Yoo, S.W.: Fuzzy bin packing problem. Fuzzy Sets Syst. 120(3), 429–434 (2001)

    Article  MathSciNet  Google Scholar 

  55. Xiao, F.: CEQD: a complex mass function to predict interference effects. IEEE Trans. Cybern. (2020). https://doi.org/10.1109/TCYB.2020.3040770

  56. Fu, C., Hou, B., Chang, W., Feng, N., Yang, S.: Comparison of evidential reasoning algorithm with linear combination in decision making. Int. J. Fuzzy Syst. 22(2), 686–711 (2020)

    Article  Google Scholar 

  57. Xiao, F., Cao, Z., Jolfaei, A.: A novel conflict measurement in decision making and its application in fault diagnosis. IEEE Trans. Fuzzy Syst. 29(1), 186–197 (2020)

    Article  Google Scholar 

  58. Fei, L., Feng, Y.: An attitudinal nonlinear integral and applications in decision making. Int. J. Fuzzy Syst. (2020). https://doi.org/10.1007/s40815-020-00862-5

  59. Xing, Y., Hwang, J.-H., Çetintemel, U., Zdonik, S.: Providing resiliency to load variations in distributed stream processing, In: Proceedings of the 32nd International Conference on Very Large Data Bases, VLDB 2006, Seoul, VLDB Endowment, pp. 775–786 (2006)

Download references

Acknowledgements

The author greatly appreciates the reviewers’ suggestions and the editor’s encouragement. This research is supported by the National Natural Science Foundation of China (No. 62003280).

Author information

Authors and Affiliations

  1. School of Big Data and Software Engineering, Chongqing University, Chongqing, 400044, China

    Fuyuan Xiao

Authors
  1. Fuyuan Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fuyuan Xiao.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xiao, F. CaFtR: A Fuzzy Complex Event Processing Method. Int. J. Fuzzy Syst. 24, 1098–1111 (2022). https://doi.org/10.1007/s40815-021-01118-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40815-021-01118-6

Keywords

Search

Navigation