Data Mining Techniques in Grid Computing Environments

Contents

Preface

List of Contributors

1 Data mining meets grid computing: Time to dance?

Alberto Sánchez, Jesús Montes, Werner Dubitzky, Julio J. Valdés, María S. Pérez and Pedro de Miguel

1.1 Introduction

1.2 Data mining

1.2.1 Complex data mining problems

1.2.2 Data mining challenges

1.3 Grid computing

1.3.1 Grid computing challenges

1.4 Data mining grid – mining grid data

1.4.1 Data mining grid: a grid facilitating large-scale data mining

1.4.2 Mining grid data: analyzing grid systems with data mining techniques

1.5 Conclusions

1.6 Summary of Chapters in this Volume

2 Data analysis services in the knowledge grid

Eugenio Cesario, Antonio Congiusta, Domenico Talia and Paolo Trunfio

2.1 Introduction

2.2 Approach

2.3 Knowledge Grid services

2.3.1 The Knowledge Grid architecture

2.3.2 Implementation

2.4 Data analysis services

2.5 Design of Knowledge Grid applications

2.5.1 The VEGA visual language

2.5.2 UML application modelling

2.5.3 Applications and experiments

2.6 Conclusions

3 GridMiner: An advanced support for e-science analytics

Peter Brezany, Ivan Janciak and A. Min Tjoa

3.1 Introduction

3.2 Rationale behind the design and development of GridMiner

3.3 Use Case

3.4 Knowledge discovery process and its support by the GridMiner

3.4.1 Phases of knowledge discovery

3.4.2 Workflow management

3.4.3 Data management

3.4.4 Data mining services and OLAP

3.4.5 Security

3.5 Graphical user interface

3.6 Future developments

3.6.1 High-level data mining model

3.6.2 Data mining query language ...