Personalized selection of web services for mobile environments

Sakkopoulos, Evangelos; Adamopoulou, Poulia; Tsakalidis, Athanasios K.; Sioutas, Spyros; Manolopoulos, Yannis

doi:10.1145/1643823.1643870

Proceedings of the International Conference on Management of Emergent Digital EcoSystems 2009

DOI: 10.1145/1643823.1643870

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Personalized selection of web services for mobile environments

Evangelos Sakkopoulos

Poulia Adamopoulou

Athanasios K. Tsakalidis

et al.

Abstract: In this paper we discuss the integration of QoS-aware search and personalization algorithms in order to discover effectual Web Services for the case of mobile web users. We present a number of novel ranking algorithms specially designed for mobile web architectures. To validate and evaluate the proposed algorithms we developed a fully working prototype for mobile-PDA devices. The prototype enables PDA users to access electronic shops and to retrieve information about their products, while going shopping. Compa… Show more

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Cited by 3 publications

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Cloud service QoS prediction via exploiting collaborative filtering and location‐based data smoothing

Tang

Zhang

Liu

et al. 2015

Concurrency and Computation

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SUMMARYTo assess the quality of services (QoS) in service selection, collaborative service QoS prediction has recently garnered increasing attention. They focus on exploring the historical QoS information generated by interactions between users and services. However, they may suffer from the data sparsity issue because interactions between users and services are usually sparse in real scenarios. They also seldom consider the network environments of users and services, which surely will affect cloud service QoS. To address the data sparsity issue and improve the QoS prediction accuracy, the following paper proposes a collaborative QoS prediction method with location-based data smoothing. The method first computes neighborhoods of users and services based on their locations which provide a basis for data smoothing. It then combines userbased and service-based collaborative filtering techniques to make QoS predictions. Experiments conducted using a real service invocation dataset validate the performance of the proposed QoS prediction method.

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Cloud service QoS prediction via exploiting collaborative filtering and location‐based data smoothing

Tang

Zhang

Liu

et al. 2015

Concurrency and Computation

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Predicting Quality of Cloud Services for Selection

Tang

Liang²,

Cao³

et al. 2015

IJGDC

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QoS-Aware Cloud Service Recommendation Using Metaheuristic Approach

et al. 2022

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As a result of the proliferation of cloud services in recent years, several service providers now offer services that are functionally identical but have different levels of service, known as Quality of Service (QoS) characteristics. Therefore, offering a cloud assistance arrangement with optimum QoS estimates that fulfilling a customer’s expectations becomes a complicated and demanding task. Several different metaheuristics are presented as potential solutions to this problem. However, most of them are unable to strike a healthy balance between exploring new territory and capitalizing on existing resources. A novel approach is suggested to balance exploration and exploitation via the use of Genetic Algorithms (GA) and the Eagle Strategy algorithm. Cloud computing provides clients with capabilities that are enabled by information technology by using services that are available on demand. To circumvent difficulties such as a delayed convergence rate or an early convergence, this technique allows for the establishment of a healthy equilibrium between exploratory and exploitative activities. The result of the experiment shows that the Eagle Strategy algorithm (ESA) and GA are better than other conventional algorithms at making a globally QoS-based Cloud Service Selection System faster.

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Personalized selection of web services for mobile environments

Cited by 3 publications

References 7 publications

Cloud service QoS prediction via exploiting collaborative filtering and location‐based data smoothing

Cloud service QoS prediction via exploiting collaborative filtering and location‐based data smoothing

Predicting Quality of Cloud Services for Selection

QoS-Aware Cloud Service Recommendation Using Metaheuristic Approach

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