QoS Based Ranking for Web Services: Fuzzy Approaches

Tran, Vuong Xuan; Tsuji, Hitomi

doi:10.1109/nwesp.2008.41

Cited by 33 publications

(19 citation statements)

References 12 publications

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“…The output of the ranking process is an ordered list of running service instances corresponding both to the extended keywords and to the requirements in terms of context expressed by the developer. The pattern-based design of the component makes it possible to extend the strategies to accommodate emerging research, e.g., [33], [34], [35] on matching and ranking that needs, however, to be adapted to consider the specifics of real-world services.…”

Section: Service Instance Rankingmentioning

confidence: 99%

Interacting with the SOA-Based Internet of Things: Discovery, Query, Selection, and On-Demand Provisioning of Web Services

Guinard

Trifa

Karnouskos³

et al. 2010

IEEE Trans. Serv. Comput.

631

335

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Abstract-The increasing usage of smart embedded devices in business blurs the line between the virtual and real worlds. This creates new opportunities to build applications that better integrate real-time state of the physical world, and hence, provides enterprise services that are highly dynamic, more diverse, and efficient. Service-Oriented Architecture (SOA) approaches traditionally used to couple functionality of heavyweight corporate IT systems, are becoming applicable to embedded real-world devices, i.e., objects of the physical world that feature embedded processing and communication. In such infrastructures, composed of large numbers of networked, resource-limited devices, the discovery of services and on-demand provisioning of missing functionality is a significant challenge. We propose a process and a suitable system architecture that enables developers and business process designers to dynamically query, select, and use running instances of real-world services (i.e., services running on physical devices) or even deploy new ones on-demand, all in the context of composite, real-world business applications.

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Section: Service Instance Rankingmentioning

confidence: 99%

Interacting with the SOA-Based Internet of Things: Discovery, Query, Selection, and On-Demand Provisioning of Web Services

Guinard

Trifa

Karnouskos³

et al. 2010

IEEE Trans. Serv. Comput.

631

335

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“…Fuzzy logic is a solution for both the computational complexity and the matching with imprecise QoS constraints. An overview of fuzzy approaches is presented in [16].…”

Section: Resultsmentioning

confidence: 99%

Translating user preferences into fuzzy rules for the automatic selection of services

Şora

Todinca

Avram

2009

2009 5th International Symposium on Applied Computational Intelligence and Informatics

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Abstract-This article proposes an approach for including user preferences and quality of service characteristics in the selection process of services. Our approach consists of a Domain ontology for the service description vocabulary, and a trader that facilitates user-preference-based service selection, combining imperfect service matching and ranking algorithms. The novelty of our approach lies in the fact that we automatically generate fuzzy rules starting from individual user preferences and use them in a fuzzy inference process that ranks the service candidates. We present our experiments to evaluate our approach using a prototype implementation of a service broker.

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“…The major limitation of these methods, however, is that they may not be able to differentiate between qualitative and non-qualitative services. Various solutions have been proposed by the service research community, such as the use of Quality of Service (QoS) information [43,42,3], user generated data (such as user ratings or feedback) [24,41,25,7], or multiple ranking criteria for decision making [38].…”

Section: Related Workmentioning

confidence: 99%