A trust-based service suggestion system using human plausible reasoning

Abedinzadeh, Sadra; Sadaoui, Samira

doi:10.1007/s10489-013-0495-8

Cited by 17 publications

(11 citation statements)

References 35 publications

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“…Probabilistic models and machine learning techniques are used in [52] to predict which quality class each Web service belongs to. Sadra et al [53] proposed a novel generic Agent Trust Management (ATM) framework ScubAA based on the theory of Human Plausible Reasoning (HPR) for managing trust in an open system. To overcome feedback subjectivity issues, an automated rating model based on expectancy-disconfirmation theory from market science is proposed [11].…”

Section: Related Work and Discussionmentioning

confidence: 99%

Effective BigData-Space Service Selection over Trust and Heterogeneous QoS Preferences

Wang

et al. 2018

IEEE Trans. Serv. Comput.

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As the number of Cloud services is growing at a tremendous speed, there is an increasing number of service providers offering similar functionalities. Selecting services with user desired non-functional properties (NFPs) becomes of significant importance but triggers a number of Big Data related research issues. First, the selection decision should deal with a large volume of service NFPs data. Second, service selection needs to reflect diverse user preferences, including both qualitative and quantitative ones. Third, the uncertainty of the network and service load leads to high variability in NFPs. Fourth, as the trust values of service NFPs are collected via historic user's feedbacks, it brings the veracity dimension to the NFPs of services. Fifth, multiple and sometimes conflicting decision objectives for optimal service selection should be balanced. An effective service selection mechanism is in demand that can tackle all the above Big Data challenges in an integrated way to handle the highly diverse QoS with significant variability along with the trust related issues giving rise to data veracity. Existing investigations focus on either users' QoS preferences or their trust concerns but fail to provide a systematic solution to integrate both criteria in the selection process. In this paper, we tackle heterogeneous preferenceand trust-based service selection by developing a novel multi-objective optimization approach to make trade-off decision between service's trust value and user's QoS preference to rank candidate Cloud services based on their match degrees with users' requirements. We conduct extensive experiments to evaluate the effectiveness and efficiency of the proposed approach.

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Section: Related Work and Discussionmentioning

confidence: 99%

Effective BigData-Space Service Selection over Trust and Heterogeneous QoS Preferences

Wang

et al. 2018

IEEE Trans. Serv. Comput.

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“…It discloses a system generating a description of the concepts based on the recipient's model, taking into account his/her current knowledge. Hierarchies' concept is also core element of ScubAA system [9], recommending best services in analyzed context, e.g. most accurate Internet search engines.…”

Section: Methodsmentioning

confidence: 99%

Supporting the Manufacturing Process of Metal Products with the Methods of Artificial Intelligence

Wilk-Kołodziejczyk

2016

Archives of Metallurgy and Materials

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The aim of this research programme was to develop a series of methods and solutions to support the decision-making process in foundry and materials engineering. The specific problems discussed included the selection of methods for data processing and knowledge representation formalisms, backed up by the creation of decision algorithms based on contemporary achievements of artificial intelligence, tailored to the needs of foundry industry and metallurgy. The manufacturing process of metal items is associated with many aspects, which affect the quality of end product. For process engineers responsible for the supervising and planning of production, an important feature is, among others, the diversified nature of numerous aspects of the knowledge acquisition and integration from distributed sources of information which, when made available in an appropriate manner, can support the improvement of manufacturing process.

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“…As a generalization of probability theory, Dempster‐Shafer evidence theory is a powerful tool for dealing with uncertain and imprecise information and has been applied in many fields, for example, decision making, risk assessment, and classification . However, some strong hypotheses and strict constraints limit the application of this theory to a large extent.…”

Section: Preliminariesmentioning

confidence: 99%

“…As a generalization of probability theory, Dempster-Shafer evidence theory is a powerful tool for dealing with uncertain and imprecise information and has been applied in many fields, for example, decision making, 20-23 risk assessment, 24,25 and classification. [26][27][28] However, some strong hypotheses and strict constraints limit the application of this theory to a large extent. For instance, the exclusiveness hypothesis requires that all the elements in the FOD be mutually exclusive, and the completeness constraint requires the sum of the basic probabilities of the mass function to equal 1.…”

Section: Dempster-shafer Evidence Theory and D-numbersmentioning

confidence: 99%

A novel framework for the reliability modelling of repairable multistate complex mechanical systems considering propagation relationships

Gao

Wang

Gao

et al. 2018

Quality & Reliability Eng

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Due to the propagation, amplification, and concatenation in a failure process, the reliabilities of repairable multistate complex mechanical systems (RMCMSs) may be affected by a significant fluctuation due to a small exception associated with a reliability indicator. Focused on the problems arising from the lack of propagation relationships among fault modes, functional components, and failure causes in conventional reliability models, a novel framework for reliability modelling is proposed to comprehensively analyse the reliabilities of RMCMSs. First, the reliability models are abstracted as weighted and directed networks with five layers. Second, an improved failure mode and effects analysis (IFMEA) method combined with the D‐number method and VIKOR approach is presented to determine the importance of reliability nodes. Third, a cut set of the reliability model is generated by any exception of a reliability indicator by considering the propagation relationships, and the reliability sensibility index is defined to characterize the fluctuations in system reliability. The effectiveness of the proposed framework is demonstrated in an actual reliability modelling application. As an intuitive method, the proposed framework inherits the advantages of conventional models but overcomes the drawbacks of these existing methods. Therefore, this method can be flexibly and efficiently used in the reliability modelling of RMCMSs. Moreover, the approach provides a foundation for comprehensive and dynamic reliability analysis and the failure mechanism mining of RMCMSs, and it can be used in other engineering applications.

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A trust-based service suggestion system using human plausible reasoning

Cited by 17 publications

References 35 publications

Effective BigData-Space Service Selection over Trust and Heterogeneous QoS Preferences

Effective BigData-Space Service Selection over Trust and Heterogeneous QoS Preferences

Supporting the Manufacturing Process of Metal Products with the Methods of Artificial Intelligence

A novel framework for the reliability modelling of repairable multistate complex mechanical systems considering propagation relationships

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