SEFAP: an efficient approach for ranking skyline web services

Ouadah, Abdelaziz; Hadjali, Allel; Nader, Fahima; Benouaret, Karim

doi:10.1007/s12652-018-0721-7

Cited by 40 publications

(20 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As observed in [57], the lesser value of the QoS criterion has a higher impact on the proposed ranking results. Therefore, low values of quality metrics have effects on the ranking results of our proposed approach.…”

Section: E Impact Of Qos Attributes Values Changes On Web Services Rmentioning

confidence: 56%

Evaluating Trust Prediction and Confusion Matrix Measures for Web Services Ranking

et al. 2020

View full text Add to dashboard Cite

To accurately rank various web services can be a very challenging task depending on the evaluation criteria used, however, it can play an important role in performing a better selection of web services afterward. This paper proposes an approach to evaluate trust prediction and confusion matrix to rank web services from throughput and response time. AdaBoostM1 and J48 classifiers are used as binary classifiers on a benchmark web services dataset. The trust score (TS) measuring method is proposed by using the confusion matrix to determine trust scores of all web services. Trust prediction is calculated using 5-Fold, 10-Fold, and 15-Fold cross-validation methods. The reported results showed that the web service 1 (WS1) was most trusted with (48.5294%) TS value, and web service 2 (WS2) was least trusted with (24.0196%) TS value by users. Correct prediction of trusted and untrusted users in web services invocation has improved the overall selection process in a pool of similar web services. Kappa statistics values are used for the evaluation of the proposed approach and for performance comparison of the two above-mentioned classifiers.

show abstract

Section: E Impact Of Qos Attributes Values Changes On Web Services Rmentioning

confidence: 56%

Evaluating Trust Prediction and Confusion Matrix Measures for Web Services Ranking

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Before our work, Li et al [67] and Ibrahim [68] performed a sensitivity analysis of QoS metrics to analyze their effects on the decision results. However, we follow the ways adopted in [69] for sensitivity analysis. Generally, the new ranking of quality metrics is influenced by the WMR score.…”

Section: Sensitivity Analysismentioning

confidence: 99%

Benchmark Dataset Selection of Web Services Technologies: A Factor Analysis

et al. 2020

View full text Add to dashboard Cite

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.

show abstract

“…There is a wide variety of applications in different environments. In [32,33], authors use skyline queries to efficiently identify the best web services. In [34], authors used a neural network to reduce the search space and efficiently identify skyline candidates which was applied in Bioinformatics [35].…”

Section: Applications Of Skyline Queriesmentioning

confidence: 99%

A Skyline-Based Decision Boundary Estimation Method for Binominal Classification in Big Data

Kalyvas

Μaragoudakis

2020

Computation

View full text Add to dashboard Cite

One of the most common tasks nowadays in big data environments is the need to classify large amounts of data. There are numerous classification models designed to perform best in different environments and datasets, each with its advantages and disadvantages. However, when dealing with big data, their performance is significantly degraded because they are not designed—or even capable—of handling very large datasets. The current approach is based on a novel proposal of exploiting the dynamics of skyline queries to efficiently identify the decision boundary and classify big data. A comparison against the popular k-nearest neighbor (k-NN), support vector machines (SVM) and naïve Bayes classification algorithms shows that the proposed method is faster than the k-NN and the SVM. The novelty of this method is based on the fact that only a small number of computations are needed in order to make a prediction, while its full potential is revealed in very large datasets.

show abstract

SEFAP: an efficient approach for ranking skyline web services

Cited by 40 publications

References 59 publications

Evaluating Trust Prediction and Confusion Matrix Measures for Web Services Ranking

Evaluating Trust Prediction and Confusion Matrix Measures for Web Services Ranking

Benchmark Dataset Selection of Web Services Technologies: A Factor Analysis

A Skyline-Based Decision Boundary Estimation Method for Binominal Classification in Big Data

Contact Info

Product

Resources

About