Coherent quality management for big data systems: a dynamic approach for stochastic time consistency

S.I.: Reliability and Quality Management in Stochastic Systems


Big data systems for reinforcement learning have often exhibited problems (e.g., failures or errors) when their components involve stochastic nature with the continuous control actions of reliability and quality. The complexity of big data systems and their stochastic features raise the challenge of uncertainty. This article proposes a dynamic coherent quality measure focusing on an axiomatic framework by characterizing the probability of critical errors that can be used to evaluate if the conveyed information of big data interacts efficiently with the integrated system (i.e., system of systems) to achieve desired performance. Herein, we consider two new measures that compute the higher-than-expected error,—that is, the tail error and its conditional expectation of the excessive error (conditional tail error)—as a quality measure of a big data system. We illustrate several properties (that suffice stochastic time-invariance) of the proposed dynamic coherent quality measure for a big data system. We apply the proposed measures in an empirical study with three wavelet-based big data systems in monitoring and forecasting electricity demand to conduct the reliability and quality management in terms of minimizing decision-making errors. Performance of using our approach in the assessment illustrates its superiority and confirms the efficiency and robustness of the proposed method.


Big data Dynamic coherent measure Optimal decision Quality management Time consistency 

JEL Classification

C02 C10 C63 



The authors would like to thank the three anonymous reviewers and the guest editor for providing valuable comments. This work was supported in part by the Ministry of Science and Technology (MOST) under Grant 106-2221-E-009-006 and Grant 106-2221-E-009-049-MY2, in part by the “Aiming for the Top University Program” of National Chiao Tung University and the Ministry of Education, Taiwan, and in part by Academia Sinica AS-105-TP-A07 and Ministry of Economic Affairs (MOEA) 106-EC-17-A-24-0619.


  1. Agarwal, R., Green, R., Brown, P., Tan, H., & Randhawa, K. (2013). Determinants of quality management practices: An empirical study of New Zealand manufacturing firms. International Journal of Production Economics, 142, 130–145.CrossRefGoogle Scholar
  2. Artzner, P., Delbaen, F., Eber, J., Heath, D., & Ku, K. (2007). Coherent multiperiod risk adjusted values and Bellman’s principle. Annals of Operations Research, 152, 5–22.CrossRefGoogle Scholar
  3. Baucells, M., & Borgonovo, E. (2013). Invariant probabilistic sensitivity analysis. Management Science, 59(11), 2536–2549.CrossRefGoogle Scholar
  4. Bion-Nadal, J. (2008). Dynamic risk measures: Time consistency and risk measures from BMO martingales. Finance and Stochastics, 12(2), 219–244.CrossRefGoogle Scholar
  5. Bion-Nadal, J. (2009). Time consistent dynamic risk processes. Stochastic Processes and their Applications, 119(2), 633–654.CrossRefGoogle Scholar
  6. Chen, Y., & Sun, E. (2015). Jump detection and noise separation with singular wavelet method for high-frequency data. Working paper of KEDGE BS.Google Scholar
  7. Chen, Y., & Sun, E. (2018). Chapter 8: Automated business analytics for artificial intelligence in big data \(@\)x 4.0 era. In M. Dehmer & F. Emmert-Streib (Eds.), Frontiers in Data Science (pp. 223–251). Boca Raton: CRC Press.Google Scholar
  8. Chen, Y., Sun, E., & Yu, M. (2015). Improving model performance with the integrated wavelet denoising method. Studies in Nonlinear Dynamics and Econometrics, 19(4), 445–467.Google Scholar
  9. Chen, Y., Sun, E., & Yu, M. (2017). Risk assessment with wavelet feature engineering for high-frequency portfolio trading. Computational Economics.
  10. Cheridito, P., & Stadje, M. (2009). Time-inconsistency of VaR and time-consistent alternatives. Finance Research Letters, 6, 40–46.CrossRefGoogle Scholar
  11. Chun, S., Shapiro, A., & Uryasev, S. (2012). Conditional value-at-risk and average value-at-risk: Estimation and asymptotics. Operations Research, 60(4), 739–756.CrossRefGoogle Scholar
  12. David, H., & Nagaraja, H. (2003). Order statistics (3rd ed.). Hoboken: Wiley.CrossRefGoogle Scholar
  13. Deichmann, J., Roggendorf, M., & Wee, D. (2015). McKinsey quarterly november: Preparing IT systems and organizations for the Internet of Things. McKinsey & Company.Google Scholar
  14. Hazen, B., Boone, C., Ezell, J., & Jones-Farmer, J. (2014). Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications. International Journal of Production Economics, 154, 72–80.CrossRefGoogle Scholar
  15. Keating, C., & Katina, P. (2011). Systems of systems engineering: Prospects and challenges for the emerging field. International Journal of System of Systems Engineering, 2(2/3), 234–256.CrossRefGoogle Scholar
  16. Liu, Y., Muppala, J., Veeraraghavan, M., Lin, D., & Hamdi, M. (2013). Data center networks: Topologies architechtures and fault-tolerance characteristics. Berlin: Springer.CrossRefGoogle Scholar
  17. Maier, M. (1998). Architecting principles for systems-of-systems. Systems Engineering, 1(4), 267–284.CrossRefGoogle Scholar
  18. Mellat-Parst, M., & Digman, L. (2008). Learning: The interface of quality management and strategic alliances. International Journal of Production Economics, 114, 820–829.CrossRefGoogle Scholar
  19. O’Neill, P., Sohal, A., & Teng, W. (2015). Quality management approaches and their impact on firms’ financial performance—An Australian study. International Journal of Production Economics.
  20. Parast, M., & Adams, S. (2012). Corporate social responsibility, benchmarking, and organizational performance in the petroleum industry: A quality management perspective. International Journal of Production Economics, 139, 447–458.CrossRefGoogle Scholar
  21. Pham, H. (2006). System software reliability. Berlin: Springer.CrossRefGoogle Scholar
  22. Riedel, F. (2004). Dynamic coherent risk measures. Stochastic Processes and their Applications, 112(2), 185–200.CrossRefGoogle Scholar
  23. Shooman, M. (2002). Reliability of computer systems and networks: Fault tolerance analysis and design. Hoboken: Wiley.CrossRefGoogle Scholar
  24. Sun, E., Chen, Y., & Yu, M. (2015). Generalized optimal wavelet decomposing algorithm for big financial data. International Journal of Production Economics, 165, 161–177.Google Scholar
  25. Sun, E., & Meinl, T. (2012). A new wavelet-based denoising algorithm for high-frequency financial data mining. European Journal of Operational Research, 217, 589–599.CrossRefGoogle Scholar
  26. Sun, W., Rachev, S., & Fabozzi, F. (2007). Fractals or I.I.D.: Evidence of long-range dependence and heavy tailedness from modeling German equity market returns. Journal of Economics and Business, 59, 575–595.CrossRefGoogle Scholar
  27. Sun, W., Rachev, S., & Fabozzi, F. (2009). A new approach for using Lèvy processes for determining high-frequency value-at-risk predictions. European Financial Management, 15(2), 340–361.CrossRefGoogle Scholar
  28. Wu, S., & Zhang, D. (2013). Analyzing the effectiveness of quality management practices in China. International Journal of Production Economics, 144, 281–289.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Business Informatics and MathematicsUniversity of MannheimMannheimGermany
  2. 2.College of Computer ScienceNational Chiao Tung University (NCTU)HsinchuTaiwan
  3. 3.KEDGE Business SchoolTalance CedexFrance

Personalised recommendations