Journal
ANNALS OF OPERATIONS RESEARCH
Volume 277, Issue 1, Pages 3-32Publisher
SPRINGER
DOI: 10.1007/s10479-018-2795-1
Keywords
Big data; Dynamic coherent measure; Optimal decision; Quality management; Time consistency
Categories
Funding
- Ministry of Science and Technology (MOST) [106-2221-E-009-006, 106-2221-E-009-049-MY2]
- Aiming for the Top University Program of National Chiao Tung University
- Ministry of Education, Taiwan
- Academia Sinica [AS-105-TP-A07]
- Ministry of Economic Affairs (MOEA) [106-EC-17-A-24-0619]
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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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