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  • 1.
    Li, Jun
    et al.
    College of Physics and Electronic Information Engineering, Wenzhou University, China, College of Computer Science, Zhejiang University, China.
    Zheng, Xiao-Lin
    College of Computer Science, Zhejiang University, China.
    Chen, Song-Tao
    College of Computer Science, Zhejiang University, China.
    Song, William Wei
    Dalarna University, School of Technology and Business Studies, Information Systems.
    Chen, De-Ren
    College of Computer Science, Zhejiang University, China.
    An efficient and reliable approach for quality-of-service-aware service composition2014In: Information Sciences, ISSN 0020-0255, E-ISSN 1872-6291, Vol. 269, p. 238-254Article in journal (Refereed)
    Abstract [en]

    With the rapidly increasing number of independently developed Web services that provide similar functionalities with varied quality of service (QoS), service composition is considered as a problem in the selection of component services that are in accordance with users' QoS requirements; a practice known as the QoS-aware service composition problem. However, current solutions are unsuitable for most real-time decision-making service composition applications required to obtain a relatively optimal result within a reasonable amount of time. These services are also unreliable (or even risky) given the open service-oriented environment. In this paper, we address these problems and propose a novel heuristic algorithm for an efficient and reliable selection of trustworthy services in a service composition. The proposed algorithm consists of three steps. First, a trust-based selection method is used to filter untrustworthy component services. Second, convex hulls are constructed to reduce the search space in the process of service composition. Finally, a heuristic global optimization approach is used to obtain the near-optimal solution. The results demonstrate that our approach obtains a close-to-optimal and reliable solution within a reasonable computation time.

  • 2. Yan, Su-Rong
    et al.
    Zheng, Xiao-Lin
    Wang, Yan
    Song, William Wei
    Dalarna University, School of Technology and Business Studies, Information Systems.
    Zhang, Wen-Yu
    A graph-based comprehensive reputation model: exploiting the social context of opinions to enhance trust in social commerce2015In: Information Sciences, ISSN 0020-0255, E-ISSN 1872-6291, Vol. 318, p. 51-72Article in journal (Refereed)
    Abstract [en]

    Social commerce is a promising new paradigm of e-commerce. Given the open and dynamic nature of social media infrastructure, the governance structures of social commerce are usually realized through reputation mechanisms. However, the existing approaches to the prediction of trust in future interactions are based on personal observations and/or publicly shared information in social commerce application. As a result, the indications are unreliable and biased because of limited first-hand information and stake-holder manipulation for personal strategic interests. Methods that extract trust values from social links among users can improve the performance of reputation mechanisms. Nonetheless, these links may not always be available and are typically sparse in social commerce, especially for new users. Thus, this study proposes a new graph-based comprehensive reputation model to build trust by fully exploiting the social context of opinions based on the activities and relationship networks of opinion contributors. The proposed model incorporates the behavioral activities and social relationship reputations of users to combat the scarcity of first-hand information and identifies a set of critical trust factors to mitigate the subjectivity of opinions and the dynamics of behaviors. Furthermore, we enhance the model by developing a novel deception filtering approach to discard "bad-mouthing" opinions and by exploiting a personalized direct distrust (risk) metric to identify malicious providers. Experimental results show that the proposed reputation model can outperform other trust and reputation models in most cases. (C) 2014 Elsevier Inc. All rights reserved.

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