Reputation Bootstrapping for Composite Services Using CP-Nets
We propose a novel framework to bootstrap the reputation of on-demand service compositions. On-demand compositions are usually context-aware and have little or no direct consumer feedback. The reputation bootstrapping of single or atomic services do not consider the topology of the composition and r...
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| Veröffentlicht in: | IEEE transactions on services computing 2022-11, Vol.15 (6), p.3513-3527 |
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| container_title | IEEE transactions on services computing |
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| creator | Mistry, Sajib Bouguettaya, Athman |
| description | We propose a novel framework to bootstrap the reputation of on-demand service compositions. On-demand compositions are usually context-aware and have little or no direct consumer feedback. The reputation bootstrapping of single or atomic services do not consider the topology of the composition and relationships among reputation-related factors. We apply Conditional Preference Networks (CP-nets) of reputation-related factors for each of component services in a composition. The reputation of a composite service is bootstrapped by the composition of CP-nets. We consider the history of invocation among component services to determine reputation-interdependence in a composition. The composition rules are constructed using the composition topology and four types of reputation-influence among component services. A heuristic-based Q-learning approach is proposed to select the optimal set of reputation-related CP-nets. Experimental results prove the efficiency of the proposed approach. |
| doi_str_mv | 10.1109/TSC.2021.3084928 |
| format | Article |
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On-demand compositions are usually context-aware and have little or no direct consumer feedback. The reputation bootstrapping of single or atomic services do not consider the topology of the composition and relationships among reputation-related factors. We apply Conditional Preference Networks (CP-nets) of reputation-related factors for each of component services in a composition. The reputation of a composite service is bootstrapped by the composition of CP-nets. We consider the history of invocation among component services to determine reputation-interdependence in a composition. The composition rules are constructed using the composition topology and four types of reputation-influence among component services. A heuristic-based Q-learning approach is proposed to select the optimal set of reputation-related CP-nets. Experimental results prove the efficiency of the proposed approach.</description><identifier>ISSN: 1939-1374</identifier><identifier>EISSN: 1939-1374</identifier><identifier>EISSN: 2372-0204</identifier><identifier>DOI: 10.1109/TSC.2021.3084928</identifier><identifier>CODEN: ITSCAD</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Biomedical imaging ; combinatorial selection ; composite services ; Composition ; CP-nets ; History ; Machine learning ; Quality of service ; Reputation bootstrapping ; reputation propagation heuristics ; Service level agreements ; Surveillance ; Topology</subject><ispartof>IEEE transactions on services computing, 2022-11, Vol.15 (6), p.3513-3527</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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On-demand compositions are usually context-aware and have little or no direct consumer feedback. The reputation bootstrapping of single or atomic services do not consider the topology of the composition and relationships among reputation-related factors. We apply Conditional Preference Networks (CP-nets) of reputation-related factors for each of component services in a composition. The reputation of a composite service is bootstrapped by the composition of CP-nets. We consider the history of invocation among component services to determine reputation-interdependence in a composition. The composition rules are constructed using the composition topology and four types of reputation-influence among component services. A heuristic-based Q-learning approach is proposed to select the optimal set of reputation-related CP-nets. Experimental results prove the efficiency of the proposed approach.</description><subject>Biomedical imaging</subject><subject>combinatorial selection</subject><subject>composite services</subject><subject>Composition</subject><subject>CP-nets</subject><subject>History</subject><subject>Machine learning</subject><subject>Quality of service</subject><subject>Reputation bootstrapping</subject><subject>reputation propagation heuristics</subject><subject>Service level agreements</subject><subject>Surveillance</subject><subject>Topology</subject><issn>1939-1374</issn><issn>1939-1374</issn><issn>2372-0204</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpNkE1LAzEQhoMoWKt3wcuC562ZfGySgwdd_IKiYttzSHcnssU2a5IK_nu3VMTTvDDPOwMPIedAJwDUXM1n9YRRBhNOtTBMH5ARGG5K4Eoc_svH5CSlFaUV09qMyPUb9tvschc2xW0IOeXo-r7bvBc-xKIO6z6kLmMxw_jVNZiKRdot69fyGXM6JUfefSQ8-51jsri_m9eP5fTl4am-mZYNM5DL1oumZV55YZyXqEBTRlE4JpdUUM15y7RyCpRsBk43nMLSg6mg8u2QkY_J5f5uH8PnFlO2q7CNm-GlZUoKkMAkDBTdU00MKUX0to_d2sVvC9TuJNlBkt1Jsr-ShsrFvtIh4h9uhBCV1PwHfIdhuw</recordid><startdate>20221101</startdate><enddate>20221101</enddate><creator>Mistry, Sajib</creator><creator>Bouguettaya, Athman</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| source | IEEE Electronic Library (IEL) |
| subjects | Biomedical imaging combinatorial selection composite services Composition CP-nets History Machine learning Quality of service Reputation bootstrapping reputation propagation heuristics Service level agreements Surveillance Topology |
| title | Reputation Bootstrapping for Composite Services Using CP-Nets |
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