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An Intelligent Approach for Context-Aware Service Selection using Machine Learning

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Mahmoud NassarAuthors Info & Claims

LOPAL '18: Proceedings of the International Conference on Learning and Optimization Algorithms: Theory and Applications

Article No.: 46, Pages 1 - 6

https://doi.org/10.1145/3230905.3230938

Published: 02 May 2018 Publication History

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Abstract

Service selection is a process to choose the services that best suit user functional and Non-functional Properties (NFP). With the increasing number of available services, users are offered a choice of competitively functional (or even identical) services. Therefore, this choice strongly depends on the NFPs and the user preferences (context) that differentiate between several competitive services. The service selection can be performed automatically and transparently to the user. In this paper, An extension of OWL-S service is proposed to take context information into account during the selection. Afterwards, we presents an intelligent approach for context-aware service selection based on Markov Decision Process, we show how to solve it using reinforcement learning techniques.

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Cited By

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Bian HChang RBhaskaran KZhang WChang C(2023)Distributed and Intelligent API Mediation Service for Enterprise-Grade Hybrid-Multicloud Computing2023 IEEE International Conference on Software Services Engineering (SSE)10.1109/SSE60056.2023.00025(1-11)Online publication date: Jul-2023
https://doi.org/10.1109/SSE60056.2023.00025
Daosabah AGuermah HNassar M(2023)User's intention and context as pertinent factors for optimal web service compositionService Oriented Computing and Applications10.1007/s11761-023-00380-w18:1(33-66)Online publication date: 23-Dec-2023
https://doi.org/10.1007/s11761-023-00380-w
Hamdi ASalim FKim DNeiat ABouguettaya A(2022)Drone-as-a-Service Composition Under UncertaintyIEEE Transactions on Services Computing10.1109/TSC.2021.306600615:5(2685-2698)Online publication date: 1-Sep-2022
https://doi.org/10.1109/TSC.2021.3066006
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LOPAL '18: Proceedings of the International Conference on Learning and Optimization Algorithms: Theory and Applications

May 2018

357 pages

ISBN:9781450353045

DOI:10.1145/3230905

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 02 May 2018

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LOPAL '18: Theory and Applications

May 2 - 5, 2018

Rabat, Morocco

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LOPAL '18 Paper Acceptance Rate 61 of 141 submissions, 43%;

Overall Acceptance Rate 61 of 141 submissions, 43%

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Bian HChang RBhaskaran KZhang WChang C(2023)Distributed and Intelligent API Mediation Service for Enterprise-Grade Hybrid-Multicloud Computing2023 IEEE International Conference on Software Services Engineering (SSE)10.1109/SSE60056.2023.00025(1-11)Online publication date: Jul-2023
https://doi.org/10.1109/SSE60056.2023.00025
Daosabah AGuermah HNassar M(2023)User's intention and context as pertinent factors for optimal web service compositionService Oriented Computing and Applications10.1007/s11761-023-00380-w18:1(33-66)Online publication date: 23-Dec-2023
https://doi.org/10.1007/s11761-023-00380-w
Hamdi ASalim FKim DNeiat ABouguettaya A(2022)Drone-as-a-Service Composition Under UncertaintyIEEE Transactions on Services Computing10.1109/TSC.2021.306600615:5(2685-2698)Online publication date: 1-Sep-2022
https://doi.org/10.1109/TSC.2021.3066006
Quan HTakahashi RYoshiaki F(2019)Dynamic Service Selection based on User Feedback in the IoT Environment2019 International Conference on Computer, Information and Telecommunication Systems (CITS)10.1109/CITS.2019.8862134(1-5)Online publication date: Aug-2019
https://doi.org/10.1109/CITS.2019.8862134

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A Backwards Composition Context Based Service Selection Approach for Service Composition

A context-aware approach for service selection using ontologies

Service Selection Using Service Clusters

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