An interval type-2 fuzzy model of compliance monitoring for quality of web service
Compliance monitoring for quality of web service (QoWS) has accuracy issues due to uncertain network behaviors. Existing models use precise computation-based methods for defining and monitoring QoWS requirements, but these methods have limited ability to handle uncertainties. Consequently, the accur...
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| Veröffentlicht in: | Annals of operations research 2021-05, Vol.300 (2), p.415-441 |
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| creator | Hasan, Mohd Hilmi Jaafar, Jafreezal Watada, Junzo Hassan, Mohd Fadzil Aziz, Izzatdin Abdul |
| description | Compliance monitoring for quality of web service (QoWS) has accuracy issues due to uncertain network behaviors. Existing models use precise computation-based methods for defining and monitoring QoWS requirements, but these methods have limited ability to handle uncertainties. Consequently, the accuracy of the monitoring results is degraded. Defining expected QoWS using exact values is unrealistic, as generally not all service requestors know what values should be specified in the contract. Therefore, this paper proposes an interval type-2 (IT2) fuzzy model for QoWS compliance monitoring because it has greater capability than precise computation methods to reduce the effects of uncertainties. IT2 also has greater capability than the traditional fuzzy sets to manage uncertainty problem due to its non-crisp membership degrees assigned to the input. The model is able to perform compliance monitoring on linguistically defined QoWS. The model is developed based on fuzzy C-means algorithm, and the number of clusters is optimized using a clustering validity index. The model is constructed based on a Mamdani fuzzy inference system. The results show that the IT2 model outperforms type-1 fuzzy and precise computation-based models in terms of the accuracy of monitoring results. This research results in more accurate and precise QoWS compliance monitoring. It also provides user-centric QoWS specifications because requestors can define their requirements using linguistic values. |
| doi_str_mv | 10.1007/s10479-019-03328-6 |
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Existing models use precise computation-based methods for defining and monitoring QoWS requirements, but these methods have limited ability to handle uncertainties. Consequently, the accuracy of the monitoring results is degraded. Defining expected QoWS using exact values is unrealistic, as generally not all service requestors know what values should be specified in the contract. Therefore, this paper proposes an interval type-2 (IT2) fuzzy model for QoWS compliance monitoring because it has greater capability than precise computation methods to reduce the effects of uncertainties. IT2 also has greater capability than the traditional fuzzy sets to manage uncertainty problem due to its non-crisp membership degrees assigned to the input. The model is able to perform compliance monitoring on linguistically defined QoWS. The model is developed based on fuzzy C-means algorithm, and the number of clusters is optimized using a clustering validity index. The model is constructed based on a Mamdani fuzzy inference system. The results show that the IT2 model outperforms type-1 fuzzy and precise computation-based models in terms of the accuracy of monitoring results. This research results in more accurate and precise QoWS compliance monitoring. It also provides user-centric QoWS specifications because requestors can define their requirements using linguistic values.</description><identifier>ISSN: 0254-5330</identifier><identifier>EISSN: 1572-9338</identifier><identifier>DOI: 10.1007/s10479-019-03328-6</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Accuracy ; Algorithms ; Business and Management ; Clustering ; Combinatorics ; Compliance ; Computation ; Fuzzy sets ; Fuzzy systems ; Methods ; Model accuracy ; Model testing ; Monitoring ; Operations research ; Operations Research/Decision Theory ; S.I.: Integrated Uncertainty in Knowledge Modelling & Decision Making 2018 ; Service oriented architecture (Software design) ; Theory of Computation ; Uncertainty ; Web services</subject><ispartof>Annals of operations research, 2021-05, Vol.300 (2), p.415-441</ispartof><rights>Springer Science+Business Media, LLC, part of Springer Nature 2019</rights><rights>COPYRIGHT 2021 Springer</rights><rights>Annals of Operations Research is a copyright of Springer, (2019). All Rights Reserved.</rights><rights>Springer Science+Business Media, LLC, part of Springer Nature 2019.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c451t-d02757f0502082ea68268b0616125947496893a0cedd6a46cb6e3c0eea034d883</citedby><cites>FETCH-LOGICAL-c451t-d02757f0502082ea68268b0616125947496893a0cedd6a46cb6e3c0eea034d883</cites><orcidid>0000-0002-4065-3968</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10479-019-03328-6$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10479-019-03328-6$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Hasan, Mohd Hilmi</creatorcontrib><creatorcontrib>Jaafar, Jafreezal</creatorcontrib><creatorcontrib>Watada, Junzo</creatorcontrib><creatorcontrib>Hassan, Mohd Fadzil</creatorcontrib><creatorcontrib>Aziz, Izzatdin Abdul</creatorcontrib><title>An interval type-2 fuzzy model of compliance monitoring for quality of web service</title><title>Annals of operations research</title><addtitle>Ann Oper Res</addtitle><description>Compliance monitoring for quality of web service (QoWS) has accuracy issues due to uncertain network behaviors. Existing models use precise computation-based methods for defining and monitoring QoWS requirements, but these methods have limited ability to handle uncertainties. Consequently, the accuracy of the monitoring results is degraded. Defining expected QoWS using exact values is unrealistic, as generally not all service requestors know what values should be specified in the contract. Therefore, this paper proposes an interval type-2 (IT2) fuzzy model for QoWS compliance monitoring because it has greater capability than precise computation methods to reduce the effects of uncertainties. IT2 also has greater capability than the traditional fuzzy sets to manage uncertainty problem due to its non-crisp membership degrees assigned to the input. The model is able to perform compliance monitoring on linguistically defined QoWS. The model is developed based on fuzzy C-means algorithm, and the number of clusters is optimized using a clustering validity index. The model is constructed based on a Mamdani fuzzy inference system. The results show that the IT2 model outperforms type-1 fuzzy and precise computation-based models in terms of the accuracy of monitoring results. This research results in more accurate and precise QoWS compliance monitoring. It also provides user-centric QoWS specifications because requestors can define their requirements using linguistic values.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Business and Management</subject><subject>Clustering</subject><subject>Combinatorics</subject><subject>Compliance</subject><subject>Computation</subject><subject>Fuzzy sets</subject><subject>Fuzzy systems</subject><subject>Methods</subject><subject>Model accuracy</subject><subject>Model testing</subject><subject>Monitoring</subject><subject>Operations research</subject><subject>Operations Research/Decision Theory</subject><subject>S.I.: Integrated Uncertainty in Knowledge Modelling & Decision Making 2018</subject><subject>Service oriented architecture (Software design)</subject><subject>Theory of Computation</subject><subject>Uncertainty</subject><subject>Web 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interval type-2 fuzzy model of compliance monitoring for quality of web service</title><author>Hasan, Mohd Hilmi ; Jaafar, Jafreezal ; Watada, Junzo ; Hassan, Mohd Fadzil ; Aziz, Izzatdin Abdul</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c451t-d02757f0502082ea68268b0616125947496893a0cedd6a46cb6e3c0eea034d883</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>Business and Management</topic><topic>Clustering</topic><topic>Combinatorics</topic><topic>Compliance</topic><topic>Computation</topic><topic>Fuzzy sets</topic><topic>Fuzzy systems</topic><topic>Methods</topic><topic>Model accuracy</topic><topic>Model testing</topic><topic>Monitoring</topic><topic>Operations research</topic><topic>Operations Research/Decision Theory</topic><topic>S.I.: Integrated Uncertainty in Knowledge Modelling & Decision Making 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Existing models use precise computation-based methods for defining and monitoring QoWS requirements, but these methods have limited ability to handle uncertainties. Consequently, the accuracy of the monitoring results is degraded. Defining expected QoWS using exact values is unrealistic, as generally not all service requestors know what values should be specified in the contract. Therefore, this paper proposes an interval type-2 (IT2) fuzzy model for QoWS compliance monitoring because it has greater capability than precise computation methods to reduce the effects of uncertainties. IT2 also has greater capability than the traditional fuzzy sets to manage uncertainty problem due to its non-crisp membership degrees assigned to the input. The model is able to perform compliance monitoring on linguistically defined QoWS. The model is developed based on fuzzy C-means algorithm, and the number of clusters is optimized using a clustering validity index. The model is constructed based on a Mamdani fuzzy inference system. The results show that the IT2 model outperforms type-1 fuzzy and precise computation-based models in terms of the accuracy of monitoring results. This research results in more accurate and precise QoWS compliance monitoring. It also provides user-centric QoWS specifications because requestors can define their requirements using linguistic values.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s10479-019-03328-6</doi><tpages>27</tpages><orcidid>https://orcid.org/0000-0002-4065-3968</orcidid></addata></record> |
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| ispartof | Annals of operations research, 2021-05, Vol.300 (2), p.415-441 |
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| source | SpringerLink Journals; Business Source Complete |
| subjects | Accuracy Algorithms Business and Management Clustering Combinatorics Compliance Computation Fuzzy sets Fuzzy systems Methods Model accuracy Model testing Monitoring Operations research Operations Research/Decision Theory S.I.: Integrated Uncertainty in Knowledge Modelling & Decision Making 2018 Service oriented architecture (Software design) Theory of Computation Uncertainty Web services |
| title | An interval type-2 fuzzy model of compliance monitoring for quality of web service |
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