Buchberger / Affenzeller / Ferscha Hagenberg Research

1. Auflage 2009
ISBN: 978-3-642-02127-5
Verlag: Springer
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 488 Seiten

BrunoBuchberger This book is a synopsis of basic and applied research done at the various re search institutions of the Softwarepark Hagenberg in Austria. Starting with 15 coworkers in my Research Institute for Symbolic Computation (RISC), I initiated the Softwarepark Hagenberg in 1987 on request of the Upper Aus trian Government with the objective of creating a scienti?c, technological, and economic impulse for the region and the international community. In the meantime, in a joint e?ort, the Softwarepark Hagenberg has grown to the current (2009) size of over 1000 R&D employees and 1300 students in six research institutions, 40 companies and 20 academic study programs on the bachelor, master's and PhD level. The goal of the Softwarepark Hagenberg is innovation of economy in one of the most important current technologies: software. It is the message of this book that this can only be achieved and guaranteed long term by 'watering the root', namely emphasis on research, both basic and applied. In this book, we summarize what has been achieved in terms of research in the various research institutions in the Softwarepark Hagenberg and what research vision we have for the imminent future. When I founded the Softwarepark Hagenberg, in addition to the 'watering the root' principle, I had the vision that such a technology park can only prosper if we realize the 'magic triangle', i.e. the close interaction of research, academic education, and business applications at one site, see Figure 1.

Autoren/Hrsg.

Buchberger, Bruno

Affenzeller, Michael

Ferscha, Alois

Haller, Michael

Jebelean, Tudor

Inhaltsverzeichnis

1;Contents;5
2;Acknowledgement;8
3;Hagenberg Research: Introduction;9
3.1;References;12
4;Chapter I Algorithms in Symbolic Computation;13
4.1;1 The Renaissance of Algorithmic Mathematics;14
4.1.1;1.1 A Bit of History;14
4.2;2 Gröbner Bases Theory for Nonlinear Polynomial Systems;24
4.2.1;2.1 The Relevance of Gröbner Bases Theory;24
4.2.2;2.2 Gröbner Bases: Basic Notions and Results;27
4.3;3 Rational Algebraic Curves – Theory and Application;32
4.3.1;3.1 What is a Rational Algebraic Curve?;32
4.3.2;3.3 Proper Parametrizations;34
4.3.3;3.4 A Parametrization Algorithm;35
4.3.4;3.5 Applications of Curve Parametrization;38
4.4;4 Computer Generated Progress in Lattice Paths Theory;41
4.4.1;4.1 Paths in the Quarter Plane;41
4.4.2;4.2 Computer Algebra Support;43
4.4.3;4.3 Gessel’s Conjecture;44
4.4.4;4.4 Lattice Paths in 3D;46
4.5;5 Symbolic Summation in Particle Physics;48
4.5.1;5.1 The Underlying Summation Principles;49
4.5.2;5.2 Example 1: Simplification of Multi-Sums;52
4.5.3;5.3 Example 2: Solving Large Recurrence Relations;55
4.6;6 Nonlinear Resonance Analysis;57
4.6.1;6.1 What is Resonance?;57
4.6.2;6.2 Kinematics and Dynamics;60
4.6.3;6.3 Highlights of the Research on the NRA;64
4.7;References;66
5;Chapter II Automated Reasoning;71
5.1;1 Introduction;71
5.2;2 Theorema: Computer-Supported Mathematical Theory Exploration;73
5.2.1;2.1 The Theorema Language and the User Interface;75
5.2.2;2.2 “Lazy Thinking”: Invention by Formulae Schemes and Failing Proof Analysis;78
5.3;3 Natural Style Proving in Theorema;82
5.3.1;3.1 S-Decomposition and the Use of Algebraic Techniques;83
5.3.2;3.2 The Theorema Set Theory Prover;88
5.4;4 Unification;91
5.4.1;4.1 General Sequence Unification;91
5.4.2;4.2 Flat Matching;93
5.4.3;4.3 Context Sequence Matching;95
5.4.4;4.4 Relations between Context and Sequence Unification;95
5.5;5 Program Verification;96
5.5.1;5.1 Some Principles of Program Verification ;97
5.5.2;5.2 Verification of Functional Programs;98
5.6;References;106
6;Chapter III Metaheuristic Optimization;110
6.1;1 Introduction;110
6.1.1;1.1 Motivation and Goal;110
6.1.2;1.2 Structure and Content;115
6.2;2 Metaheuristic Optimization Techniques;116
6.2.1;2.1 Simulated Annealing;117
6.2.2;2.2 Tabu Search;118
6.2.3;2.3 Iterated Local Search;120
6.2.4;2.4 Evolutionary Algorithms;121
6.2.5;2.5 Scatter Search;122
6.2.6;2.6 Further Metaheuristics;123
6.2.7;2.7 Hybrid Metaheuristics;124
6.3;3 Algorithmic Advances Based Upon Genetic Algorithms;125
6.3.1;3.1 The Unique Selling Points of Genetic Algorithms;125
6.3.2;3.2 Schema Theorem and Building Block Hypothesis;126
6.3.3;3.3 Stagnation and Premature Convergence;128
6.3.4;3.4 Offspring Selection (OS);130
6.3.5;3.5 Consequences Arising out of O spring Selection;133
6.4;4 Route Planning;135
6.4.1;4.1 The Vehicle Routing Problem;137
6.4.2;4.2 Heuristic algorithms;140
6.4.3;4.3 Metaheuristic Approaches;141
6.5;5 Genetic Programming Based System Identification;143
6.5.1;5.1 Genetic Programming;143
6.5.2;5.2 Data Based Modeling and Structure Identification;146
6.5.3;5.3 Application Example: Time Series Analysis;148
6.5.4;5.4 Application Example: Solving Classification Problems;150
6.5.5;5.5 Analysis of Population Dynamics in Genetic Programming ;152
6.5.6;5.6 Data Mining and Genetic Programming;153
6.6;6 Conclusion and Future Perspectives;155
6.7;References;157
7;Chapter IV Software Engineering – Processes and Tools;163
7.1;1 Introduction;163
7.2;2 Software Process Engineering;165
7.2.1;2.1 Concepts Related to Software Process Engineering;168
7.2.2;2.2 Software Process Engineering Research Challenges and Application-oriented Research at SCCH;176
7.3;3 Software Quality Engineering;190
7.3.1;3.1 Concepts and Perspectives in Engineering of Software Quality;191
7.3.2;3.2 Management and Automation of Software Testing;194
7.4;4 Software Architecture Engineering;206
7.4.1;4.1 General Research Areas and Challenges;207
7.4.2;4.2 Software Architecture Management – Languages and Tools;210
7.4.3;4.3 Software Architectures for Industrial Applications;216
7.5;5 Domain-Specific Languages and Modeling;220
7.5.1;5.1 Overview of the Field;221
7.5.2;5.2 Modeling and Code Generation;224
7.5.3;5.3 Textual Domain-Specific Languages;228
7.5.4;5.4 End-User Programming;229
7.6;References;232
8;Chapter V Data-Driven and Knowledge-Based Modeling;242
8.1;1 Introduction;242
8.2;2 Fuzzy Logics and Fuzzy Systems;243
8.2.1;2.1 Motivation;243
8.2.2;2.3 Fuzzy Systems;244
8.3;3 Data-Driven Fuzzy Systems;247
8.3.1;3.1 Motivation;247
8.3.2;3.2 Data-Driven Fuzzy Modeling Approaches;248
8.3.3;3.3 Regularization and Parameter Selection;252
8.4;4 Evolving Fuzzy Systems and On-line Modeling;253
8.4.1;4.1 Motivation and Solutions;253
8.4.2;4.2 The FLEXFIS Family;255
8.4.3;4.3 Handling Drifts and Unlearning E ect in Data Streams;259
8.5;5 Creating Comprehensible Fuzzy Regression Models;260
8.5.1;5.1 Motivation;260
8.5.2;5.2 The Underlying Language;260
8.5.3;5.3 Rule Induction;262
8.5.4;5.4 Post-Optimization of Fuzzy Rule Bases;264
8.6;6 Support Vector Machines and Kernel-Based Design;265
8.6.1;6.1 Kernels as Similarities: Motivation and Recent Developments;265
8.6.2;6.2 Support Vector Machines;267
8.7;7 Applications;269
8.7.1;7.1 On-Line Fault Detection at Engine Test Benches;269
8.7.2;7.2 On-Line Image Classification in Surface Inspection Systems;272
8.7.3;7.3 Application of SVMs to Texture Analysis;276
8.7.4;Acknowledgements;278
8.8;References;278
9;Chapter VI Information and Semantics in Databases and on the Web;285
9.1;1 Introduction;285
9.2;2 Ontologies;287
9.3;3 Semantic Networks;293
9.4;4 Adaptive Modeling;298
9.5;5 Web Information Extraction;304
9.6;6 Similarity Queries and Case Based Reasoning;323
9.7;7 Data Warehouses;330
9.8;References;333
10;Chapter VII Parallel, Distributed, and Grid Computing;336
10.1;1 Introduction;336
10.2;2 Parallel Symbolic Computation;345
10.3;3 Grid Computing;352
10.4;4 GPU Computing for Computational Intelligence;369
10.5;References;377
11;Chapter VIII Pervasive Computing;382
11.1;1 What is Pervasive Computing?;383
11.2;2 Ensembles of Digital Artifacts;385
11.3;3 Quantitative Space: Zones-of-Influence;393
11.4;4 Qualitative Space: Spatiotemporal Relations;397
11.5;5 Middleware for Space Awareness;405
11.6;6 Embodied Interaction;411
11.7;7 Outlook;424
11.8;References;428
12;Chapter IX Interactive Displays and Next-Generation Interfaces;435
12.1;1 Interactive Surfaces;437
12.2;2 Design Challenges;443
12.3;3 Design and Implementation of a Multi-Display Environment for Collaboration;455
12.4;4 Conclusions;470
12.5;References;471
13;Index;475