Ranking Web Services using Web Service Popularity Score

Elfirdoussi, Selwa; Jarir, Zahi; Quafafou, Mohamed

doi:10.4018/ijitwe.2014040105

Cited by 17 publications

(6 citation statements)

References 19 publications

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“…They implemented the proposed approach to a travel application and found that the proposed method took low execution time. Before the proposal of the latter proposed approach, Elfirdoussi [23] also involved the web services and pertinent QoS aspects to select web services. A user's request was matched to the best web service that reduced time in the composition and selection of web services.…”

Section: Trust-based Web Services Selectionmentioning

confidence: 99%

Machine Learning Methods for Trust-based Selection of Web Services

2022

KSII TIIS

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Web services instances can be classified into two categories, namely trusted and untrusted from users. A web service with high throughput (TP) and low response time (RT) instance values is a trusted web service. Web services are not trustworthy due to the mismatch in the guaranteed instance values and the actual values achieved by users. To perform web services selection from users' attained TP and RT values, we need to verify the correct prediction of trusted and untrusted instances from invoked web services. This accurate prediction of web services instances is used to perform the selection of web services. We propose to construct fuzzy rules to label web services instances correctly. This paper presents web services selection using a well-known machine learning algorithm, namely REPTree, for the correct prediction of trusted and untrusted instances. Performance comparison of REPTree with five machine learning models is conducted on web services datasets. We have performed experiments on web services datasets using a ten k-fold cross-validation method. To evaluate the performance of the REPTree classifier, we used accuracy metrics (Sensitivity and Specificity). Experimental results showed that web service (WS1) gained top selection score with the (47.0588%) trusted instances, and web service (WS2) was selected the least with (25.00%) trusted instances. Evaluation results of the proposed web services selection approach were found as (asymptotic sig. = 0.019), demonstrating the relationship between final selection and recommended trust score of web services.

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Section: Trust-based Web Services Selectionmentioning

confidence: 99%

Machine Learning Methods for Trust-based Selection of Web Services

2022

KSII TIIS

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“…Despite this change in the service selection process, most of the service selection research work still treats cloud-based services as traditional on-premise services whose QoS values are mainly decided by one service provider [7], or some researchers study the selection problem mainly for infrastructure services [8] or platform services [9]. The QoS matching process is a crucial process in several operations such as service selection [10][11] [12][13], ranking [14][15] [16], composition [17][18] [19] and recommendation [20][21]. In the service selection domain, researchers rely on claimed QoS values by providers or on monitoring tools to obtain the values.…”

Section: Motivationmentioning

confidence: 99%

End-to-end QoS computation for vertical service composition in the cloud

Karim¹

2021

Preprint

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Cloud services are designed to provide users with different computing models such as software-as-a-Services (SaaS), Infrastructure-as-a-Service (IaaS), Data-as-a-Service (DaaS), and other IT related services (denoted as XaaS). Easy, scalable and on-demand cloud services are offered by cloud providers to users. With the prevalence of different types of cloud services, the task of selecting the best cloud service solution has become more and more challenging. Cloud service solutions are offered through a collaboration of different cloud services at different cloud layers. This type of collaborations is denoted as vertical service composition. Quality of Service (QoS) properties are used as differentiating factors for selecting the best services among functionally equivalent services. In this thesis, we introduce a new service selection framework for the cloud which vertically matches services offered by different cloud providers based on users’ end-to-end QoS requirements. Functional requirements can be satisfied by the required cloud service (software service, platform service, etc) alone. However, users’ QoS requirements must be satisfied using all involved cloud services in a service composition. Therefore, in order to select the best cloud service compositions for users, QoS values of these compositions must be end-to-end. To tackle the problem of computing unknown end-to-end QoS values of vertical cloud service compositions for target users (for whom these values are computed), we propose two strategies: QoS mapping and aggregation and QoS prediction. The former deals with new cloud service compositions with no prior history. Using this strategy, we can map users’ QoS requirements onto different cloud layers and then we aggregate QoS values guaranteed by cloud providers to estimate end-to-end QoS values. The latter deals with cloud service compositions for which QoS data have been recorded in an active system. Using the QoS prediction strategy, we utilize historical QoS data of previously invoked service compositions and other service and user information to predict end-to-end QoS values. The presented experimental results demonstrate the importance of considering vertically composed cloud services when computing end-to-end QoS values as opposed to traditional prediction approaches. Our QoS prediction approach outperforms other prediction approaches in terms of the prediction accuracy by at least 20%.

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“…In [23] proposed a hybrid recommender system which was based on userrecommender interaction. In [32] proposed web services recommendations system using collaborative filtering techniques and in [33,34,35] proposed recommendations system ranking. In [36,37,38] proposed recommendation systems for analysis of symbolic data.…”

Section: Recommendation Systemmentioning

confidence: 99%

Preference Oriented Mining Techniques for Location based Point Search

Sakib

2019

SSRN Journal

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Ranking Web Services using Web Service Popularity Score

Cited by 17 publications

References 19 publications

Machine Learning Methods for Trust-based Selection of Web Services

Machine Learning Methods for Trust-based Selection of Web Services

End-to-end QoS computation for vertical service composition in the cloud

Preference Oriented Mining Techniques for Location based Point Search

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