臺灣博碩士論文加值系統

在本研究中，我們進行了兩階段以網際網路服務品質為導向的網際網路服務選擇模型研究 - QCMA模型(QCMA: QoS Consensus Moderation Approach) 與 FMG-QCMA模型 (Fuzzy Multi- Groups-Based QCMA)。第一階段的QCMA模型著重以辨識網際網路參與者對網際網路服務品質感知的相似度，進而確認這些參與者是否具高相似度，並根據已確認之高相似度參與者對網際網路服務品質因素偏好優先順序，決定這個高相似性群體對於網際網路服務選擇之決策模型。第二階段的FMG-QCMA模型著重於思考不同之消費者其差異過大的性格背景偏好差異，進而研究結合多維度的網際網路服務品質因素結構的分群演算法，建立更有效率的多群組架構網際網路選擇的決策模型。同時，在分群結構之中，因相似度些微低於分群比對的相似合格度，就被裁定網際網路服務品質意見為不相似，本研究也提出了模糊邊界的概念，將近似合格邊界的網際網路品質意見，納入為該群組之模糊相似意見，進而更明確的掌握分群有效性，避免分群失真現象。

本研究之模型適合任一種網際網路服務應用之目標客群分析，如線上旅行社、網際網路商城、網路拍賣會等網際網路應用服務。

In this research, two stages of modeling for QoS-aware selection of web service were established – QCMA (QoS Consensus Moderation Approach) and FMG-QCMA (Fuzzy Multi- Groups-Based QCMA). QCMA was proposed as the first stage in order to indentifying a group of participants by their high similarity and obtaining the group preference over all QoS attributes. FMG-QCMA was proposed as second stage in order to thinking over the distinct background and preference over QoS attributes among all web service participants. For this purpose a more efficient multi-attributes-based multi-groups clustering approach was studied for developing multi-groups-based QoS-aware selection model of web service. Also, the concept of fuzzy boundary, which is used for preventing possible omission of some opinions that should be treated as “similar” to group centre but cannot beyond the threshold distance defined in clustering criterion, was thought over.

The models in the research can be applied to “target customers analysis” on any web service application such as e-tourist agency, e-mall or e-auction.

摘 要 i
ABSTRACT ii
誌 謝 iii
Content v
Table vii
Figure viii
Symbols ix
Chapter 1. Introduction - QoS-aware Selection of Web Services 1
1.1 RESEARCH BACKGROUND AND MOTIVATION 1
1.2 TWO STAGES FOR RESEARCH OBJECTIVE: QCMA AND FMG-QCMA 3
1.3 STRUCTURE OF THE DESSERTATION 6
Chapter 2. Literatures Review 8
2.1 WEB SERVICE 8
2.1.1 Web Service Definition 8
2.1.2 XML (Extensible Markup Language) 9
2.1.3 SOAP (Simple Object Access Protocol) 10
2.1.4 WSDL (Web Service Description Language) 11
2.1.5 UDDI (Universal Description Discovery & Integration) 12
2.2 QOS-AWARE WEB SERVICE SELECTION – EXISTING SOLUTIONS 14
2.2.1 QoS for Web Service 14
2.2.2 Current Literatures about QoS-aware Web Service Selection 19
2.2.3 Research Foundation - MFDM 21
2.3 MULTI-ATTRIBUTES-BASED OPINIONS CLUSTERING - EXISTING SOLUTIONS 23
Chapter 3. QoS Consensus Moderation Approach (QCMA) 29
3.1 APPROACHES, FRAMEWORK AND BEHAVIOR 29
3.1.1 Similarity Aggregation Method (SAM) 30
3.1.2 Resolution Method for Group Decision Problems (RMGDP) 34
3.1.3 QCMA Functions Enhancement and Behavior 37
3.2 VALIDATION AND EVALUATION 42
3.2.1 Similarity Analysis via SAM in QCMA 42
3.2.2 Preference Analysis via RMGDP 44
3.3 REVIEW ON QCMA 48
Chapter 4. Fuzzy Multi-Groups-based QCMA (FMG-QCMA) 50
4.1 APPROACHES, FRAMEWORK AND BEHAVIOR 50
4.1.1 System Functions Deployment in FMG-QCMA 52
4.1.2 System Behavior of FMG-QCMA 56
4.1.3 FMGSAM and Multi-Groups RMGDP 61
4.1.4 Precision and Efficiency 64
4.2 VALIDATION AND EVALUATION 65
4.2.1 Reaching Consensus: FMGSAM Process 67
4.2.2 Reaching Consensus: RMGDP Process 69
4.2.3 Marketing Web Service 71
4.2.4 Process of FMG-QCMA Moderation 72
4.3 REVIEW ON FMG-QCMA: PRECISION AND EFFICIENCY 74
Chapter 5. Conclusion 76
References 79
Appendix A: Raw Data for QCMA Validation 84
Appendix B: Algorithm Fuzzy Clustering 87
Appendix C: Algorithm SimVerifier 89
Appendix D: Algorithm Clustering Verification 91
個人簡歷 94

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