Semantic-based dynamic service adaptation in context-aware mobile cloud learning

Self-adaptable system concerns on service adaptation whenever errors persist within the system. Changes in contextual information such as networks or sensors will affect the system's effectiveness because the service adaptation process is not comprehensively handled in those contexts. Besides,...

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Main Authors: Muhamad, S., Admodisastro, N., Osman, H., Ali, N. M.
Format: Article
Published: Walter de Gruyter GmbH 2022
Online Access:http://psasir.upm.edu.my/id/eprint/103164/
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author Muhamad, S.
Admodisastro, N.
Osman, H.
Ali, N. M.
author_facet Muhamad, S.
Admodisastro, N.
Osman, H.
Ali, N. M.
author_sort Muhamad, S.
building UPM Institutional Repository
collection Online Access
description Self-adaptable system concerns on service adaptation whenever errors persist within the system. Changes in contextual information such as networks or sensors will affect the system's effectiveness because the service adaptation process is not comprehensively handled in those contexts. Besides, the correctness to get the most equivalence services to be substituted is limitedly being addressed from previous works. A dynamic service adaptation framework is introduced to monitor and run a reasoning control to solve these issues. Hence, this paper presents a case study to proof the dynamic service adaptation framework that leverages on semantic-based approach in a context-aware environment. The evaluation of the case study resulted in a significant difference for the effectiveness at a 95% confidence level, which can be interpreted to confirm that the framework is promising to be used in operating dynamic adaptation process in a pervasive environment.
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institution Universiti Putra Malaysia
institution_category Local University
last_indexed 2025-11-15T13:41:03Z
publishDate 2022
publisher Walter de Gruyter GmbH
recordtype eprints
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spelling upm-1031642024-06-28T09:31:07Z http://psasir.upm.edu.my/id/eprint/103164/ Semantic-based dynamic service adaptation in context-aware mobile cloud learning Muhamad, S. Admodisastro, N. Osman, H. Ali, N. M. Self-adaptable system concerns on service adaptation whenever errors persist within the system. Changes in contextual information such as networks or sensors will affect the system's effectiveness because the service adaptation process is not comprehensively handled in those contexts. Besides, the correctness to get the most equivalence services to be substituted is limitedly being addressed from previous works. A dynamic service adaptation framework is introduced to monitor and run a reasoning control to solve these issues. Hence, this paper presents a case study to proof the dynamic service adaptation framework that leverages on semantic-based approach in a context-aware environment. The evaluation of the case study resulted in a significant difference for the effectiveness at a 95% confidence level, which can be interpreted to confirm that the framework is promising to be used in operating dynamic adaptation process in a pervasive environment. Walter de Gruyter GmbH 2022 Article PeerReviewed Muhamad, S. and Admodisastro, N. and Osman, H. and Ali, N. M. (2022) Semantic-based dynamic service adaptation in context-aware mobile cloud learning. Cybernetics and Information Technologies, 22 (3). pp. 93-110. ISSN 1314-4081 https://sciendo.com/article/10.2478/cait-2022-0030 10.2478/cait-2022-0030
spellingShingle Muhamad, S.
Admodisastro, N.
Osman, H.
Ali, N. M.
Semantic-based dynamic service adaptation in context-aware mobile cloud learning
title Semantic-based dynamic service adaptation in context-aware mobile cloud learning
title_full Semantic-based dynamic service adaptation in context-aware mobile cloud learning
title_fullStr Semantic-based dynamic service adaptation in context-aware mobile cloud learning
title_full_unstemmed Semantic-based dynamic service adaptation in context-aware mobile cloud learning
title_short Semantic-based dynamic service adaptation in context-aware mobile cloud learning
title_sort semantic-based dynamic service adaptation in context-aware mobile cloud learning
url http://psasir.upm.edu.my/id/eprint/103164/
http://psasir.upm.edu.my/id/eprint/103164/
http://psasir.upm.edu.my/id/eprint/103164/