Behind the Curtain: Service Selection via Trust in Composite Services

Hang, Chung-Wei; Kalia, Anup K.; Singh, Munindar P.

doi:10.1109/icws.2012.96

Cited by 18 publications

(8 citation statements)

References 23 publications

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“…This approach overcomes the dependency of user's feedback, but has an overhead of maintaining the dynamics of the composition and the association coefficient among web services. A contribution-based method is presented for propagating reputation from a composite service to its constituent services (Hang et al, 2012). It requires predefined weights to be assigned to constituent services at the beginning of composition.…”

Section: Related Workmentioning

confidence: 99%

A mixed opinion-based reputation prediction approach for reliable web service discovery

Rathore

Suman

2016

IJBIS

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Prediction of service reputation is an important research issue to include reliable services in service discovery and composition. A user of the service plays an important role in the prediction of service reputation. Therefore, feedback ratings provided by service users cannot be completely avoided. Most of the existing approaches fully trust on service users' feedback rating for reputation prediction, which often leads to bias towards positive or negative ratings. In this paper, a mixed opinion-based reputation prediction approach is proposed, which uses theoretical t-probability distribution and propositional logic to assess and predict the service reputation. The proposed approach combines users' feedback ratings and run time access rate to minimise the probable bias towards feedback ratings provided by users. Experimental result shows that proposed approach provides effective solution for prediction of service reputation, which can be helpful in performing reliable service discovery and composition.Reference to this paper should be made as follows: Rathore, M. and Suman, U. (2016) 'A mixed opinion-based reputation prediction approach for reliable web service discovery', Int.

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Section: Related Workmentioning

confidence: 99%

A mixed opinion-based reputation prediction approach for reliable web service discovery

Rathore

Suman

2016

IJBIS

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“…Previous works (e.g., [27]) established ways to model trust from indirect experience but this is beyond the scope of our paper. In this work, we follow the existing works [16], [35] that the service trust could be directly collected via service consumers' feedbacks into a central QoS registry. Note that similar trust evaluation mechanism has been used in real life E-commerce reputation systems, e.g., eBay, Sporas, Histos, Epinions, etc [30].…”

Section: Definition 5 (Multi-dimensional Qos Model)mentioning

confidence: 99%

“…The settings of QoS attributes for our synthetical dataset are shown in Table 3. Although the service trust data can be directly or indirectly collected [15], due to the lack of publicly available real service trust data [35], exiting research works mainly use artificial synthetic data for service selection experiments [3], [15], [17], [19], [35]. Meanwhile, the synthetic data can avoid bias and guarantee the objectivity of the experiment, if the artificial data is approximately consistent with the real data.…”

Section: Data Setmentioning

confidence: 99%

“…Meanwhile, the synthetic data can avoid bias and guarantee the objectivity of the experiment, if the artificial data is approximately consistent with the real data. In this paper, similar to [15], [17], [35], the trust values for the services in QWS and synthetic dataset are randomly generated in a normal distribution. For both QWS and synthetic dataset, we also randomly generate user's requirements with QoS prosperities and relative importance for the QoS attributes.…”

Section: Data Setmentioning

confidence: 99%

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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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“…When service customers select desired Web services from a cluster of Web services with the same function, Web services' nonfunctional characteristics are very important criteria. Namely, consumers select desired services not only by matching functions but also by evaluating nonfunctional characteristics such as quality of service (QoS) and trust [5].…”

Section: Introductionmentioning

confidence: 99%

A Customer-Centric Trust Evaluation Model for Personalized Service Selection

Zhang

Fan

2018

Scientific Programming

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Trust is a very important criterion when service customers select desired Web services from a cluster of Web services with the same function. Most existing trust models cannot effectively implement personalized service selection with regard to consumer preferences and expectations. This paper designs a novel trust management method based on peer-to-peer network and presents a customer-centric trust evaluation model for personalized service selection. The trust evaluation model firstly maintains consumerto-consumer trust values that are calculated according to preference similarity between customers, secondly gathers ratings on services submitted by other consumers, then synthesizes customer-to-customer trust and these ratings to generate personalized consumer-to-service trust, and finally selects the desired services according to the expected trust levels presented by customers. This paper conducts some experiments to demonstrate the details of service selection. Experimental results show that this model has good applicability to implement personalized service selection. The proposed model well simulates the reality.

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Behind the Curtain: Service Selection via Trust in Composite Services

Cited by 18 publications

References 23 publications

A mixed opinion-based reputation prediction approach for reliable web service discovery

A mixed opinion-based reputation prediction approach for reliable web service discovery

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

A Customer-Centric Trust Evaluation Model for Personalized Service Selection

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