Abstract
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.




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A six sigma process is one in which 99.99966% of all opportunities are statistically expected to be free of defects (i.e., 3.4 defective features per million opportunities).
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Not because of making a round number.
References
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.
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.
Baucells, M., & Borgonovo, E. (2013). Invariant probabilistic sensitivity analysis. Management Science, 59(11), 2536–2549.
Bion-Nadal, J. (2008). Dynamic risk measures: Time consistency and risk measures from BMO martingales. Finance and Stochastics, 12(2), 219–244.
Bion-Nadal, J. (2009). Time consistent dynamic risk processes. Stochastic Processes and their Applications, 119(2), 633–654.
Chen, Y., & Sun, E. (2015). Jump detection and noise separation with singular wavelet method for high-frequency data. Working paper of KEDGE BS.
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.
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.
Chen, Y., Sun, E., & Yu, M. (2017). Risk assessment with wavelet feature engineering for high-frequency portfolio trading. Computational Economics. https://doi.org/10.1007/s10614-017-9711-7.
Cheridito, P., & Stadje, M. (2009). Time-inconsistency of VaR and time-consistent alternatives. Finance Research Letters, 6, 40–46.
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.
David, H., & Nagaraja, H. (2003). Order statistics (3rd ed.). Hoboken: Wiley.
Deichmann, J., Roggendorf, M., & Wee, D. (2015). McKinsey quarterly november: Preparing IT systems and organizations for the Internet of Things. McKinsey & Company.
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.
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.
Liu, Y., Muppala, J., Veeraraghavan, M., Lin, D., & Hamdi, M. (2013). Data center networks: Topologies architechtures and fault-tolerance characteristics. Berlin: Springer.
Maier, M. (1998). Architecting principles for systems-of-systems. Systems Engineering, 1(4), 267–284.
Mellat-Parst, M., & Digman, L. (2008). Learning: The interface of quality management and strategic alliances. International Journal of Production Economics, 114, 820–829.
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. https://doi.org/10.1016/j.ijpe.2015.07.015i.
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.
Pham, H. (2006). System software reliability. Berlin: Springer.
Riedel, F. (2004). Dynamic coherent risk measures. Stochastic Processes and their Applications, 112(2), 185–200.
Shooman, M. (2002). Reliability of computer systems and networks: Fault tolerance analysis and design. Hoboken: Wiley.
Sun, E., Chen, Y., & Yu, M. (2015). Generalized optimal wavelet decomposing algorithm for big financial data. International Journal of Production Economics, 165, 161–177.
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.
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.
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.
Wu, S., & Zhang, D. (2013). Analyzing the effectiveness of quality management practices in China. International Journal of Production Economics, 144, 281–289.
Acknowledgements
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.
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Chen, YT., Sun, E.W. & Lin, YB. Coherent quality management for big data systems: a dynamic approach for stochastic time consistency. Ann Oper Res 277, 3–32 (2019). https://doi.org/10.1007/s10479-018-2795-1
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DOI: https://doi.org/10.1007/s10479-018-2795-1
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