Exploiting Web service geographical neighborhood for collaborative QoS prediction

Chen, Zhen; Shen, Limin; Feng, Ling

doi:10.1016/j.future.2016.09.022

Cited by 37 publications

(27 citation statements)

References 32 publications

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“…We provide an example to explain the reason. Suppose there are four service items and a user u with response time vector (2,4,3,9). We make an assumption that we do not know response time value 9 of the fourth service item, i.e., we treat it as a missing value and the new experimental response time vector of u is (2,4,3, null).…”

Section: B the Densification Of User-item Matrixmentioning

confidence: 99%

“…Every value of (4,8,6,18) is double of that of (2,4,3,9). Similarity between (2,4,3,9) and (4,8,6,18) is 1 and similarity between (2,4,3, null) and (4,8,6,18) is 0.478 by the calculation of formula (1). and .…”

Section: B the Densification Of User-item Matrixmentioning

confidence: 99%

“…(2) Suppose user u only has one similar user w with response time vector (6,12,9,27). Every value of (6,12,9,27) is triple of that of (2,4,3,9).…”

Section: B the Densification Of User-item Matrixmentioning

confidence: 99%

“…In the study of [8], physical location of service provider and network status are considered for accurate QoS predictions. Geographical information of users is used to improve QoS prediction accuracy by [9]. Because these methods need special environment information, their universality is restricted.…”

Section: Introductionmentioning

confidence: 99%

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Response Time Prediction of IoT Service Based on Time Similarity

Yang¹,

Zhang²

2017

Journal of Computing Science and Engineering

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In the field of Internet of Things (IoT), smarter embedded devices offer functions via web services. The Quality-of-Service (QoS) prediction is a key measure that guarantees successful IoT service applications. In this study, a collaborative filtering method is presented for predicting response time of IoT service due to time-awareness characteristics of IoT. First, a calculation method of service response time similarity between different users is proposed. Then, to improve prediction accuracy, initial similarity values are adjusted and similar neighbors are selected by a similarity threshold. Finally, via a densified user-item matrix, service response time is predicted by collaborative filtering for current active users. The presented method is validated by experiments on a real web service QoS dataset. Experimental results indicate that better prediction accuracy can be achieved with the presented method.

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Section: B the Densification Of User-item Matrixmentioning

confidence: 99%

Section: B the Densification Of User-item Matrixmentioning

confidence: 99%

“…(2) Suppose user u only has one similar user w with response time vector (6,12,9,27). Every value of (6,12,9,27) is triple of that of (2,4,3,9).…”

Section: B the Densification Of User-item Matrixmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Response Time Prediction of IoT Service Based on Time Similarity

Yang¹,

Zhang²

2017

Journal of Computing Science and Engineering

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“…Many tools, frameworks, approaches [20] and models have been introduced to improve web service testing. As a result, a few methodologies [10,21,22] are recommended to examine the nature of these techniques and tools. This research depends on essential analysis of two testing approaches: black box [23,24] and white box [24] to test web service [25].…”

Section: Introductionmentioning

confidence: 99%

Web Service Testing Techniques: A Systematic Literature Review

Ghani¹,

Kadir²,

Mustafa³

2019

IJACSA

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These days continual demands on loosely coupled systems have web service gives basic necessities to deliver resolution that are adaptable and sufficient to be work at runtime for maintaining the high quality of the system. One of the basic techniques to evaluate the quality of such systems is through testing. Due to the rapid popularization of web service, which is progressing and continuously increasing, testing of web service has become a basic necessity to maintain high quality of web service. The testing of the performance of Web service based applications is attracting extensive attention. In order to evaluate the performance of web services, it is essential to evaluate the QoS (Quality of Service) attributes such as interoperability, reusability, auditability, maintainability, accuracy and performance to improve the quality of service. The purpose of this study is to introduce the systematic literature review of web services testing techniques to evaluate the QoS attributes to make the testing technique better. With the intention of better testing quality in web services, this systematic literature review intends to evaluate what QoS parameters are necessary to provide better quality assurance. The focus of systematic literature is also to make sure that quality of testing can be encouraged for the present and future. Consequently, the main attention and motivation of the study is to provide an overview of recent research efforts of web service testing techniques from the research community. Each testing technique in web services has identified apparent standards, benefits, and restrictions. This systemic literature review provides a different testing resolution to industry to decide which testing technique is the most efficient and effective with the testing assignment agenda with available resources. As for the significance, it can be said that web service testing technique are still broadly open for improvements.

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A new model for trustworthy web service QoS prediction

Hashemi

Ghafouri

Hung

2021

Concurrency and Computation

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The number of web services available on the internet has exploded, and as a result, the number of services with the same functionality has exploded as well. Therefore, selecting the best web service from functionally similar services is a critical task in the web service domain. The Quality of Service (QoS) is one of the most common criteria used to select the best web service. Collaborative filtering (CF) has been utilized in several studies to predict the values of QoS attributes of web services for each user in a personalized way. The QoS histories of other users are employed in these methods to predict the QoS values of the active user. Although these methods function well and produce acceptable prediction results, the accuracy of their predictions can be harmed by incorrect data provided by untrustworthy users. In this study, we propose a new model that reduces the impact of unreliable user data, resulting in a trustworthy prediction. This model can be applied to any existing prediction method. In experiments, the proposed model was applied to seven known prediction methods. The results indicate that this model is able to eliminate the impact of unreliable users.

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Exploiting Web service geographical neighborhood for collaborative QoS prediction

Cited by 37 publications

References 32 publications

Response Time Prediction of IoT Service Based on Time Similarity

Response Time Prediction of IoT Service Based on Time Similarity

Web Service Testing Techniques: A Systematic Literature Review

A new model for trustworthy web service QoS prediction

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