Intro
Preface
Acknowledgements
Contents
Contributors
Part I Methods and Techniques
1 A Vertical Fragmentation Method for Multimedia Databases Considering Content-Based Queries
1.1 Introduction
1.2 Background
1.3 State of the Art
1.4 Design of CBRVF
1.4.1 CBRVF
1.4.2 Web Application Design
1.5 Results and Discussion
1.6 Conclusion and Future Work
References
2 An Approach Based on Process Mining Techniques to Support Software Development
2.1 Introduction
2.2 Background
2.3 Related Work
2.4 Framework

2.4.1 Phase 1: Event Log Management
2.4.2 Phase 2: Process Model Discovery
2.4.3 Phase 3: Statistics
2.5 Results
2.5.1 Case of a Purchase Order Process
2.5.2 Case of an Air Quality Monitoring System Process
2.6 Conclusions
References
3 Analysis of Canonical Heuristic Methods for the Optimization of an Investment Portfolio
3.1 Introduction
3.2 Evolutionary Algorithms
3.3 Investment Portfolio
3.4 Theoretical Scaffolding
3.5 Genetic Algorithm
3.6 Differential Evolution
3.7 Artificial Immunological System
3.8 Methodology
3.9 Results

3.10 Conclusions
References
4 Design of a Dynamic Horizontal Fragmentation Method for Multimedia Databases
4.1 Introduction
4.2 Background
4.3 Related Works
4.4 Design of a Dynamic Horizontal Fragmentation Method for Multimedia Databases
4.5 Results and Discussion
4.6 Conclusion
References
5 Efficient Archiving Method for Handling Preferences in Constrained Multi-objective Evolutionary Optimization
5.1 Introduction
5.2 Problem Statement
5.3 Multi-objective Evolutionary Algorithms
5.3.1 Algorithms of Multi-Objective Evolutionary Optimization

5.3.2 Preference-Based MOEAs
5.3.3 Assessing Performance
5.4 Proposal
5.4.1 Archiving Regions of Interest
5.5 Experimental Step
5.5.1 Problems to Be Solved
5.5.2 Algorithms for Comparison
5.5.3 Parameter Settings
5.6 Results and Discussion
5.6.1 Results on Unconstrained Problems (DTLZ)
5.6.2 Results on Constrained Problems (C-DTLZ)
5.6.3 Results on Real-World Multi-Objective Problems
5.7 Conclusions and Future Work
References
6 Evaluation of Machine Learning Techniques for Malware Detection
6.1 Introduction
6.2 Related Work
6.3 Background

6.3.1 Machine Learning Techniques
6.3.2 Measurement
6.4 Methodology
6.4.1 Data Preprocessing
6.4.2 Data Representation
6.4.3 Model Training/Testing
6.5 Results
6.5.1 Data Sets
6.5.2 Performance
6.6 Conclusions
References
7 Implementation of Reinforcement-Learning Algorithms in Autonomous Robot Navigation
7.1 Introduction
7.2 Systematic Review of the Literature
7.2.1 Heuristic Algorithms
7.2.2 Applications of Reinforcement Learning
7.2.3 Synthesis and Considerations
7.3 Characteristics of Reinforcement Learning Algorithms
7.4 Methodology