Method for Combining Data Farming and Data Mining in a Logistics Assistance System for Materials Trading Networks Based on Graph Databases
Cuvillier Verlag eBooks
Published on 11. August 2025
314 pages
978-3-68952-314-5 (ISBN)
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08/2025
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Content
- Intro
- Contents
- Introduction
- Decision Support in Materials Trading Networks
- Fundamentals of Materials Trading Networks
- Logistics und Supply Chains
- Materials Trading Networks
- Key Performance Indicators in Materials Trading Networks
- Logistics Tasks in Materials Trading
- Data, Information, and Knowledge
- Demarcation of the Terms Data, Information, and Knowledge
- Classification of Data in Materials Trading Networks
- Logistics Assistance Systems in the Context of Materials Trading Networks
- Fundamentals of Information Systems
- Decision Support Systems
- Structure and Methods of Logistics Assistance Systems
- Data Management in Materials Trading Networks
- Database Systems
- Relational and Non-Relational Databases
- Fundamentals of Graph Databases
- Methods in the Context of Simulation-based Data Generation
- Simulation-based Data Generation
- Fundamentals of Simulation
- Data Farming
- Procedure Models in Data Farming
- Phases in Data Farming Procedure Models
- Analysis of Simulation Result Data
- Knowledge Discovery and Data Mining
- Graph Algorithms and Graph Mining
- Graph-based Methods in Materials Trading Networks
- Problem Statement and Research Questions
- Limitations of Previous Research Activities
- Research Approach and Delimitation
- Research Questions
- Data Farming in Logistics Assistance Systems for Materials Trading Networks
- Overview of the Methodological Framework
- Research Design
- Structure and Conceptual Design of the Method
- Architecture of the Logistics Assistance System
- Software Components of the Farming for Mining Logistics Assistance System
- Graph Database System
- Simulation Tool
- Knowledge Discovery Tool
- Method Initialization
- Development of a Specific Procedure Model for Simulation-Based Data Generation
- Derivation of Requirements for Simulation-Based Data Generation
- Selection of a Procedure Model
- Adaptation of the Selected Procedure Model
- Integration of Verification and Validation in the Context of the Method
- Knowledge Discovery in Simulation Result Data in Graph Databases
- Knowledge Discovery in the Context of the Method
- Integration of a Procedure Model for Knowledge Discovery
- Derivation of Requirements and Selection of a Procedure Model for Knowledge Discovery
- Adaptation of a Procedure Model for Knowledge Discovery
- Pattern Verification and Validation using Simulation
- Prototypical Implementation of the Developed Method
- Evaluation of Farming for Mining
- Concept and Goals of the Evaluation
- Introduction to the Use Case
- Application Field 1: Initialization
- Application Field 2: Simulation-based Data Generation
- Task Definition
- Input Data and Model Development
- Design of Experiments
- Experiments and Output Data
- Evaluation
- Verification and Validation
- Application Field 3: Knowledge Discovery and Decision Support
- Data Mining Preparation and Application
- Processing and Visualization of Results for Decision Support
- Verification and Validation
- Critical Reflection
- Summary and Future Research
- References
- List of Figures
- List of Tables
- List of Algorithms
- List of Acronyms
- Data Mining and Graph Mining Tasks, Methods, and Algorithms
- Stochastic Fundamentals
- Descriptive Statistics
- Probability Distributions and Population Parameters
- Inferential Statistics
- Verification and Validation
- Triangle Model for Verification and Validation
- Techniques for Verification and Validation
- Procedure Model for Knowledge Discovery in Databases
- Published Research Publications (Peer-Reviewed)
- Leere Seite
- Leere Seite
