QoS-Aware Video Transcoding Service Selection Process

2015 ◽  
Vol 1 (1) ◽  
pp. 61-68
Author(s):  
Nawaf O. Alsrehin ◽  
Stephen W. Clyde
Keyword(s):  
Selection Process ◽  
Service Selection ◽  
Video Transcoding ◽  
Transcoding Service

scholarly journals A User-Centric QoS-Based Web Service Selection Framework

2021 ◽  
Author(s):  
Delnavaz Mobedpour
Keyword(s):  
Query Language ◽  
Focal Point ◽  
Selection Process ◽  
Service Selection ◽  
Mixed Integer ◽  
User Support ◽  
Web Service Selection ◽  
Selection Framework ◽  
Description Languages ◽  
The One

With the proliferation of web services, the selection process, especially the one based on the non-functional properties (e.g. Quality of Service – QoS attributes) has become a more and more important step to help requestors locate a desired service. There have been many research works proposing various QoS description languages and selection models. However, the end user is not generally the focal point of their designs and the user support is either missing or lacking in these systems. The QoS language sometimes is not flexible enough to accommodate users’ various requirements and is too complex so that it puts extra burden on users. In order to solve this problem, in this thesis we design a more expressive and flexible QoS query language (QQL) targeted for non-expert users, together with the user support on formulating queries and understanding services in the registry. An enhanced selection model based on Mixed Integer Programming (MIP) is also proposed to handle the QQL queries. We performed experiments with a real QoS dataset to show the effectiveness of our framework.

scholarly journals QoS-based data analytic service selection: a comparative study of different learning models

2021 ◽  
Author(s):  
Bayan Alghofaily
Keyword(s):  
Learning Algorithm ◽  
Selection Process ◽  
Service Selection ◽  
Machine Learning Algorithms ◽  
Good Prediction ◽  
Web Service Selection ◽  
Good Prediction Accuracy ◽  
The Impact ◽  
The Given ◽  
Data Analytic

QoS-based web service selection has been studied in the service computing community for some time; however, data characteristics are not considered. In this work, we have studied the use of different machine learning algorithms as meta-learners in predicting the performance of data analytic services for the given dataset. We used a meta-learning algorithm to incorporate meta-features in the selection process and we used clustering services as an example of data analytic services. We have also investigated the impact of the number of data features on the performance of the meta-learners. We found that, out of the 5 classification models, SVM showed the best results in predicting the recommended service for the given dataset with an accuracy of 78%. When it comes to regression models, MLP was the best regressor. We recommend considering only simple meta-features that can be collected for most datasets, as those proved to be sufficient to achieve good prediction accuracy.

A Collaborative QoS-Aware Service Evaluation Method Among Multi-Users for a Shared Service

2012 ◽  
Vol 9 (1) ◽  
pp. 30-50 ◽  
Author(s):  
Wanchun Dou ◽  
Chao Lv ◽  
Xuyun Zhang ◽  
Jinjun Chen
Keyword(s):  
Evaluation Method ◽  
Selection Process ◽  
Service Selection ◽  
Service Evaluation ◽  
Multi Criteria Decision Making ◽  
End User ◽  
Analytic Hierarchy ◽  
Shared Service ◽  
Hierarchy Process

In service selection, an end user often has his or her personal preferences imposing on a candidate service’s non-functional properties. For a service selection process promoted by a group of users, candidate services are often evaluated by a group of end users who may have different preferences or priorities. In this situation, it is often a challenging effort to make a tradeoff among various preferences or priorities of the users. In view of this challenge, a multi-criteria decision-making method, named AHP (Analytic Hierarchy Process), is introduced to transform both qualitative personal preferences and users’ priorities into numeric weights. Furthermore, a QoS-aware service evaluation method is presented for a shared service’s co-selection taking advantage of AHP theory. At last, a case study is presented to demonstrate the feasibility of the method.

The role of developers’ social relationships in improving service selection

2016 ◽  
Vol 12 (4) ◽  
pp. 477-503 ◽  
Author(s):  
Devis Bianchini ◽  
Valeria De Antonellis ◽  
Michele Melchiori
Keyword(s):  
Social Network ◽  
Social Relationships ◽  
Web Application ◽  
Selection Process ◽  
Service Selection ◽  
Third Party ◽  
Data Service ◽  
Application Development ◽  
Content Type ◽  
Data Services

Purpose Modern Enterprise Web Application development can exploit third-party software components, both internal and external to the enterprise, that provide access to huge and valuable data sets, tested by millions of users and often available as Web application programming interfaces (APIs). In this context, the developers have to select the right data services and might rely, to this purpose, on advanced techniques, based on functional and non-functional data service descriptive features. This paper focuses on this selection task where data service selection may be difficult because the developer has no control on services, and source reputation could be only partially known. Design/methodology/approach The proposed framework and methodology are apt to provide advanced search and ranking techniques by considering: lightweight data service descriptions, in terms of (semantic) tags and technical aspects; previously developed aggregations of data services, to use in the selection process of a service the past experiences with the services when used in similar applications; social relationships between developers (social network) and their credibility evaluations. This paper also discusses some experimental results regarding the plan to expand other experiments to check how developers feel using the approach. Findings In this paper, a data service selection framework that extends and specializes an existing one for Web APIs selection is presented. The revised multi-layered model for data services is discussed and proper metrics relying on it, meant for supporting the selection of data services in a context of Web application design, are introduced. Model and metrics take into account the network of social relationships between developers, to exploit them for estimating the importance that a developer assigns to other developers’ experience. Originality/value This research, with respect to the state of the art, focuses attention on developers’ social networks in an enterprise context, integrating the developers’ credibility assessment and implementing the social network-based data service selection on top of a rich framework based on a multi-perspective model for data services.

scholarly journals QoS-based data analytic service selection: a comparative study of different learning models

2021 ◽  
Author(s):  
Bayan Alghofaily
Keyword(s):  
Learning Algorithm ◽  
Selection Process ◽  
Service Selection ◽  
Machine Learning Algorithms ◽  
Good Prediction ◽  
Web Service Selection ◽  
Good Prediction Accuracy ◽  
The Impact ◽  
The Given ◽  
Data Analytic

QoS-based web service selection has been studied in the service computing community for some time; however, data characteristics are not considered. In this work, we have studied the use of different machine learning algorithms as meta-learners in predicting the performance of data analytic services for the given dataset. We used a meta-learning algorithm to incorporate meta-features in the selection process and we used clustering services as an example of data analytic services. We have also investigated the impact of the number of data features on the performance of the meta-learners. We found that, out of the 5 classification models, SVM showed the best results in predicting the recommended service for the given dataset with an accuracy of 78%. When it comes to regression models, MLP was the best regressor. We recommend considering only simple meta-features that can be collected for most datasets, as those proved to be sufficient to achieve good prediction accuracy.

A QoS Aware, Cognitive Parameters Based Model for the Selection of Semantic Web Services

2010 ◽  
pp. 320-335
Author(s):  
Sandeep Kumar ◽  
Kuldeep Kumar
Keyword(s):  
Web Services ◽  
Semantic Web ◽  
Web Service ◽  
Selection Process ◽  
Service Selection ◽  
Semantic Web Services ◽  
Semantic Web Service ◽  
Web Service Selection ◽  
The One ◽  
Selection Of

Semantic Web service selection is considered as the one of the most important aspects of semantic web service composition process. The Quality of Service (QoS) and cognitive parameters can be a good basis for this selection process. In this paper, we have presented a hybrid selection model for the selection of Semantic Web services based on their QoS and cognitive parameters. The presented model provides a new approach of measuring the QoS parameters in an accurate way and provides a completely novel and formalized measurement of different cognitive parameters.

Evaluating Health Information Services

2011 ◽  
pp. 1-13 ◽  
Author(s):  
Umit Topacan ◽  
Nuri Basoglu ◽  
Tugrul U. Daim
Keyword(s):  
Decision Making ◽  
Health Service ◽  
Health Services ◽  
Health Information ◽  
Analytical Hierarchy Process ◽  
Selection Process ◽  
Service Selection ◽  
Information Services ◽  
Hierarchy Process

The objective of the chapter is to explore the factors that affect users’ preferences in the health service selection process. In the study, 4 hypothetical health services were designed by randomly selecting levels of 16 attributes and these services was evaluated by the potential users. Analytical Hierarchy Process (AHP), one of the decision making methods, was used to assess and select the best alternative.

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