Consumer oriented web services ranking and classification

Bouasker, Taycir; Langar, Mahjoub

doi:10.1109/icc.2017.7997263

Cited by 3 publications

(2 citation statements)

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“…The dataset C [31], [32], contains nine quality metrics such as reliability, response time, throughput, best practices, documentation, availability, success-ability, compatibility, and latency of 8 web services. This dataset is known as the 'quality of web services'(QWS) dataset.…”

Section: Dataset Cmentioning

confidence: 99%

Benchmark Dataset Selection of Web Services Technologies: A Factor Analysis

et al. 2020

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Web services have emerged as an accessible technology with the standard 'Extensible Mark Up' (XML) language, which is known as 'Web Services Description Language' WSDL. Web services have become a promising technology to promote the interrelationship between service providers and users. Web services users' trust is measured by quality metrics. Web service quality metrics vary in many benchmark datasets used in the existing studies. The selection of a benchmark dataset is problematic to classify and retest web services. This paper proposes a method to rank web services quality metrics for the selection of benchmark web services datasets. To measure the diversity in quality metrics, factor analysis with Varimax rotation and scree plot is a well-established method. We use factor analysis to determine percentage variance among principal factors of four benchmark datasets. Our results showed that the two-factor solution explained 94.501, 76.524, and 45.009% variances in datasets A, B, and D, respectively. A three-factor solution explained 85.085% variance in dataset C. Reliability, and response time quality metrics were predicted as the most dominating quality metrics that contributed to explain the percentage variance in four datasets. Our proposed web metric ranking (WMR) method resulted in reliability as the topmost web metric with (57.62%) score and latency web metric at the bottom-most with (3.60%) score. The proposed WMR method showed a high (96.17%) ranking precision. Obtained results verified that factor solutions after reducing the dimensions could be generalized and used in the quality improvement of web services. In future works, the authors plan to focus on a dataset with dominating quality metrics to perform regression testing of web services. INDEX TERMS Factor analysis, quality metrics, rotated loading, reliability, response time, regression testing, web services.

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Section: Dataset Cmentioning

confidence: 99%

Benchmark Dataset Selection of Web Services Technologies: A Factor Analysis

et al. 2020

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“…ThecompleteranksequenceisthengeneratedaccordingtothemissingQoSprediction.Furthermore, thenextlevelranksequenceispredictedusingtheParticleSwarmOptimizationAlgorithm.Most usersareinterestedintop-levelservicesinrecommendedservices.Therefore,thetop-levelranking wasoptimizedmorethanthelower-rankedservices. (Bouasker & Langar, 2017) proposed ranking and classifying web services based on nonfunctional attributes in consumer perspectives. Several QoS attributes were grouped into two categories:positiveandnegativeattributes.Forexample,throughputisapositiveQoSattributewhose valueincreasemeanshighquality,whereasresponsetimeisanegativeQoSattributewhosevalue increasemeanstheQoSwilldecrease.Theycompletewebservices'rankingbasedonthreeinner levelsofscorecomputationcomparedwithexistingrecordedapproaches (Al-Masri&Mahmoud, 2008).…”

Section: Literature Surveymentioning

confidence: 99%

An Approach to Aggregate the Partial Rank List of Web Services in E-Business

Mareeswari

Sathiyamoorthy

2019

International Journal of E-Business Research

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In the present web era, efficient and topmost outcomes of applications, such as recommender systems, search engines, voting and other ranking applications fascinate web users. Web services maintain communication among applications and applications to end users. In E*Trade, the support system evolves to suggest services based on the user's browser preferences. Services thus are ranked depending on the quality of service of the corresponding service from a user perspective. There are adequate services that are accessible, but users utilize only their desired services and give their ranking. In the process of final rank generation, merging the long partial ranked list by heterogeneous web service users is not adequate in current research articles. This approach applies the efficient methods of Markov chain for this dynamic context, and validating using real datasets and results showed the efficiency of this approach. This ranking approach engages the consumers to choose their services in a short span in the decision-making process in this competitive electronic business system.

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