Details

Autor(en) / Beteiligte

• Dubitzky, Werner,
• Dubitzky, Werner,

Titel

Data mining techniques in grid computing environments [electronic resource]

Auflage

1st edition

Link zum Volltext

• Wiley Online Library - AutoHoldings Books

Beschreibungen/Notizen

• Description based upon print version of record.
• Includes bibliographical references and index.
• Data Mining Techniques in Grid Computing Environments; Contents; Preface; List of Contributors; 1 Data mining meets grid computing: Time to dance?; 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
• 2.1 Introduction2.2 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; 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 management3.4.4 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; 3.6.3 Distributed mining of data streams; 3.7 Conclusions; 4 ADaM services: Scientific data mining in the service-oriented architecture paradigm; 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; 4.5.1 Implementation architecture; 4.5.2 Workflow example; 4.5.3 Implementation issues
• 4.6 Mining grid services4.6.1 Architecture components; 4.6.2 Workflow example; 4.7 Summary; 5 Mining for misconfigured machines in grid systems; 5.1 Introduction; 5.2 Preliminaries and related work; 5.2.1 System misconfiguration detection; 5.2.2 Outlier detection; 5.3 Acquiring, pre-processing and storing data; 5.3.1 Data sources and acquisition; 5.3.2 Pre-processing; 5.3.3 Data organization; 5.4 Data analysis; 5.4.1 General approach; 5.4.2 Notation; 5.4.3 Algorithm; 5.4.4 Correctness and termination; 5.5 The GMS; 5.6 Evaluation; 5.6.1 Qualitative results; 5.6.2 Quantitative results
• 5.6.3 Interoperability5.7 Conclusions and future work; 6 FAEHIM: Federated Analysis Environment for Heterogeneous Intelligent Mining; 6.1 Introduction; 6.2 Requirements of a distributed knowledge discovery framework; 6.2.1 Category 1: knowledge discovery specific requirements; 6.2.2 Category 2: distributed framework specific requirements; 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; 6.9.1 Evaluating the framework accuracy
• 6.9.2 Evaluating the running time of the framework
• 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 inte
• English