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.

CaFtR: A Fuzzy Complex Event Processing Method

Information Quality for Intuitionistic Fuzzy Values with Its Application in Decision Making

How to measure the complexity of physiological signals in biological system is an open problem. Various entropy algorithms have been presented, but most of them fail to account for the complexity of time series with high accuracy. In this paper, the concept of Belief Entropy-of-Entropy (BEoE) is introduced, it expands entropy of entropy (EoE) into belief structure, and computes quadratic belief entropy to characterize the complexity of biological systems based on multiple time scales. The influence of inherent complex fluctuation, length bound, correlation of time windows, etc. is considered in the BEoE analysis. Application and discussion demonstrate that BEoE has better accurateness and applicability than many existing entropy algorithms. 

Belief entropy-of-entropy and its application in the cardiac interbeat interval time series analysis

Uncertainty is inevitable in the decision-making process of real applications. Quantum mechanics has become an interesting and popular topic in predicting and explaining human decision-making behaviors, especially regarding interference effects caused by uncertainty in the process of decision making, due to the limitations of Bayesian reasoning. In addition, complex evidence theory (CET), as a generalized Dempster-Shafer evidence theory, has been proposed to represent and handle uncertainty in the framework of the complex plane, and it is an effective tool in uncertainty reasoning. Particularly, the complex mass function, also known as a complex basic belief assignment in CET, is complex-value modeled, which is superior to the classical mass function in expressing uncertain information. CET is considered to have certain inherent connections with quantum mechanics since both are complex-value modeled and can be applied in handling uncertainty in decision-making problems. In this article, therefore, by bridging CET and quantum mechanics, we propose a new complex evidential quantum dynamical (CEQD) model to predict interference effects on human decision-making behaviors. In addition, uniform and weighted complex Pignistic belief transformation functions are proposed, which can be used effectively in the CEQD model to help explain interference effects. The experimental results and comparisons demonstrate the effectiveness of the proposed method. In summary, the proposed CEQD method provides a new perspective to study and explain the interference effects involved in human decision-making behaviors, which is significant for decision theory. 

CEQD: A Complex Mass Function to Predict Interference Effects

Combining time-series evidence: A complex network model based on a visibility graph and belief entropy

Multi-sensor data fusion (MSDF) is an efficient technology to enhance the performance of the system with the involvement of different kinds of sensors, which are broadly utilized in many fields at present. However, the data obtained from multi-sensors may have different degrees of uncertainty in the practical applications. Evidence theory is very useful to convey and manage uncertainty without a priori probability, so that it has been proverbially adopted in the information fusion fields. However, in the face of conflicting evidences, it has the possibility of producing counterintuitive results via conducting the Dempster's combination rule (DCR). To solve the above-mentioned issue, a hybrid MSDF method is exploited through integrating a newly defined evidential credibility measure of evidences based on prospect theory and the evidence theory. More specifically, a series of concepts for the evidential credibility measure are first presented, including the local credibility degree, global credibility degree, evidential credibility estimation and credibility prospect value function to comprehensively describe the award and punish grades in terms of credible evidence and incredible evidence, respectively. Based on the above researches, an appropriate weight for each evidence can be obtained. Ultimately, the weight of each evidence is leveraged to amend the primitive evidences before conducting DCR. The results attained in the experiments demonstrate that the hybrid MSDF approach is efficient and superior to handle conflict evidences as well as the application in data fusion problems.

Fuyuan Xiao

Papers

Evidence combination based on prospect theory for multi-sensor data fusion.