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Fuzzy matching of comprehensive service specifications / by Marie Christin Platenius. Supervisor: Prof. Dr. Wilhelm Schäfer. Paderborn, 2016
Inhalt
Abstract
Zusammenfassung
Danksagung
Table of Contents
1 Introduction
1.1 Service Matching
1.2 Running Example
1.3 Problem Statement
1.4 Overview of the Solution
1.5 Application Scenarios
1.5.1 On-The-Fly Computing
1.5.2 Mobile Apps
1.5.3 Business-to-Business Applications
1.5.4 Industry 4.0
1.6 Structure of this Thesis
2 Foundations
2.1 Software Components and Services
2.2 Service Markets
2.3 Service Specification
2.3.1 Signatures
2.3.2 Behavioral Specifications
2.3.3 Privacy
2.3.4 Reputation
2.4 Service Matching
2.4.1 Ontological Signature Matching
2.4.2 Behavioral Matching Approaches
2.4.3 Privacy Matching
2.4.4 Reputation Matching
2.5 Foundations in Aggregation
2.6 Measures for Validation
3 Comprehensive Service Matching
3.1 Scientific Contributions
3.2 Example Scenario
3.3 Requirements
3.4 Matching Process Models
3.5 Overview of the MatchBox Workflow
3.6 Phase 1: Setup
3.6.1 Integration of Matchers
3.6.2 Integration of Aggregation Strategies
3.7 Phase 2: Process Configuration
3.7.1 a) Manual Process Configuration
3.7.2 b) Process Generation
3.8 Phase 3: Execution
3.8.1 Input Assignment
3.8.2 Execution
3.8.3 Matching Results Inspection
3.8.4 Matching Results Validation
3.9 Prototype Implementation
3.9.1 Architecture and Technologies
3.9.2 Main Features
3.10 Validation
3.10.1 Validation Questions
3.10.2 Case Study 1.1: Matching Process Variability
3.10.3 Case Study 1.2: Matcher Integration Effort
3.10.4 Discussion
3.10.5 Satisfaction of the Requirements
3.10.6 Threats to Validity
3.11 Limitations
3.12 Related Work
3.12.1 Survey Procedure
3.12.2 Comparison of Processes in Service Matching Approaches
3.13 Conclusion
4 Fuzzy Service Matching
4.1 Scientific Contributions
4.2 Foundations of Fuzzy Modeling
4.2.1 Fuzzy Sets and Fuzzy Logic
4.2.2 Possibility Theory
4.3 Requirements
4.4 Fuzziness Classification
4.4.1 Fuzziness Types
4.4.2 Fuzziness Sources
4.4.3 Fuzziness Occurrences
4.5 Fuzzy Matching Procedure
4.6 Fuzzy Matching Approaches
4.6.1 Fuzzy Matching Based on Fuzzy Logic
4.6.2 Fuzziness Quantification as an Extension
4.7 Prototype Implementation
4.7.1 Architecture and Technologies
4.7.2 Main Features
4.8 Validation
4.8.1 Validation Questions
4.8.2 Case Study 2.1: Evaluation of Fuzzy Reputation Matching based on Fuzzy Logic
4.8.3 Case Study 2.2: Evaluation of Fuzziness Quantification
4.8.4 Discussion Summary
4.8.5 Satisfaction of the Requirements
4.8.6 Threats to Validity
4.9 Limitations
4.10 Related Work
4.10.1 Survey Procedure
4.10.2 Comparison of Fuzzy Service Matching Approaches
4.10.3 Fuzziness in Related Software Engineering Disciplines
4.11 Conclusions
5 Integrated and Comprehensive Fuzzy Matching Processes
5.1 Scientific Contributions
5.2 Combining Comprehensive and Fuzzy Matching
5.2.1 Requirements
5.2.2 Framework Extensions for Fuzzy Matching
5.2.3 Aggregating Fuzzy Matching Results
5.2.4 Coping with Imperfect Information by Self-Adaption
5.2.5 Prototype Implementation
5.3 Back-Transformation of Matching Results
5.3.1 Requirements
5.3.2 Step 1: Transformation into a Matcher's Input
5.3.3 Step 2: Matching Process Generation and Enhanced Matching
5.3.4 Step 3: Back-Transformation and Representation
5.3.5 Prototype Implementation
5.3.6 Limitations
5.3.7 Related Work
5.4 Integrating a Matcher into a Service Market Architecture
5.4.1 Requirements
5.4.2 Integrating a Matcher based on Architectural Tactics
5.4.3 Exemplary Application: Service Matchers in OTF Markets
5.4.4 Related Work
5.5 Conclusions
6 Conclusion
6.1 Results and Conclusions
6.2 Future Research Challenges
Bibliography
Own Publications
Supervised Theses
Literature
Referenced Websites
List of Figures
List of Tables
A Metamodels
A.1 MatchBox Main Metamodels
A.2 Matching Process Generator Metamodels
A.3 Service Specification Metamodels
A.3.1 Privacy Specification
A.3.2 Reputation Specification
B Integrated Matchers and Aggregation Strategies
B.1 Matcher Definitions
B.2 Aggregation Strategy Definitions
C Literature Surveys
C.1 Keywords
C.2 Exclusion Criteria
D Evaluation Data
D.1 Validation of Matching Processes
D.2 Validation of Fuzzy Matching
D.3 Validation of Market Integration
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