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Informationen zum Autor Werner Dubitzky, PhD, is Chair of Bioinformatics at the Biomedical Sciences Research Institute in the Faculty of Life and Health Sciences at the University of Ulster. His research investigates systems biology, knowledge management in biology, grid computing, and data mining. Krzysztof Kurowski, PhD, leads the Applications Department at Poznan Supercomputing and Networking Center in Poland. His research is focused on the modeling of advanced applications, scheduling, and resource management in networked environments. Klappentext Based around eleven international real life case studies and including contributions from leading experts in the field this groundbreaking book explores the need for the grid-enabling of data mining applications and provides a comprehensive study of the technology, techniques and management skills necessary to create them. This book provides a simultaneous design blueprint, user guide, and research agenda for current and future developments and will appeal to a broad audience; from developers and users of data mining and grid technology, to advanced undergraduate and postgraduate students interested in this field. Zusammenfassung Based around eleven international real life case studies and including contributions from leading experts in the field this groundbreaking book explores the need for the grid-enabling of data mining applications and provides a comprehensive study of the technology, techniques and management skills necessary to create them. Inhaltsverzeichnis 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. 1.3 Grid computing. 1.4 Data mining grid - mining grid data. 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. 2.4 Data analysis services. 2.5 Design of Knowledge Grid applications. 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 GridMiner. 3.5 Graphical user interface. 3.6 Future developments. 3.7 Conclusions. 4. ADaM services: scientific data mining in the service-oriented architecture paradigm (Rahul Ramachandran, Sara Graves, John Rushing, Ken Keiser, Manil Maskey, Hong Lin and Helen Conover). 4.1 Introduction. 4.2 ADaM system overview. 4.3 ADaM toolkit overview. 4.4 Mining in a service-oriented architecture. 4.5 Mining Web services. 4.6 Mining grid services. 4.7 Summary. 5. Mining for misconfigured machines in grid systems (Noam Palatin, Arie Leizarowitz, Assaf Schuster and Ran Wolff). 5.1 Introduction. 5.2 Preliminaries and related work. 5.3 Acquiring, pre-processing and storing data. 5.4 Data analysis. 5.5 The GMS. 5.6 Evaluation. 5.7 Conclusions and future work. 6. FAEHIM: Federated Analysis Environment for Heterogeneous Intelligent Mining (Ali Shaikh Ali and Omer F. Rana). 6.1 Introduction. 6.2 Requirements of a distributed knowledge discovery framework. 6.3 Workflow-based knowledge discovery. 6.4 Data mining toolkit. 6.5 Data mining service framework. 6.6 Distributed data mining services. 6.7 Data manipulation tools. 6.8 Availability. 6.9 Empirical experiments. 6.10 ...
