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Semantic Grid: Model, Methodology, and Applications 2008 ed. [Hardback]

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  • Formāts: Hardback, 230 pages, height x width: 235x155 mm, weight: 529 g, XIII, 230 p., 1 Hardback
  • Sērija : Advanced Topics in Science and Technology in China
  • Izdošanas datums: 12-Sep-2008
  • Izdevniecība: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3540794530
  • ISBN-13: 9783540794530
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Semantic Grid: Model, Methodology, and Applications 2008 ed.Palielināt
  • Formāts: Hardback, 230 pages, height x width: 235x155 mm, weight: 529 g, XIII, 230 p., 1 Hardback
  • Sērija : Advanced Topics in Science and Technology in China
  • Izdošanas datums: 12-Sep-2008
  • Izdevniecība: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3540794530
  • ISBN-13: 9783540794530
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9783540794530.html
Keywords:

Semantic Grid: Model, Methodology, and Applications introduces to the science, core technologies, and killer applications. First, scientific issues of semantic grid systems are covered, followed by two basic technical issues, data-level semantic mapping, and service-level semantic interoperating. Two killer applications are then introduced to show how to build a semantic grid for specific application domains. Although this book is organized in a step by step manner, each chapter is independent. Detailed application scenarios are also presented. In 1990, Prof. Wu invented the first KB-system tool, ZIPE, based on C on a SUN platform. He proposed the first coupling knowledge representing model, Couplingua, which embodies Rule, Frame, Semantic Network and Nerve Cell Network, and supports symbol computing and data processing computing. His current focus is on semantic web, grid & ubiquitous computing, and their applications in the life sciences.



This book introduces the science and core technologies of semantic grid systems. It also presents two killer applications to show how to build a semantic grid for specific application domains.

Recenzijas

From the reviews:









"This book is organized proposal of the various components of a system able to integrate grid computing and the semantic Web . The references are listed at the end of each chapter. A short analytical index is included. A notable point of this book is the organized presentation of many topics that occur in such a large area as semantic grids. This book is a definite asset for developers and students." (G. Gini, ACM Computing Reviews, January, 2009)

1 Introduction
1
1.1 Background
1
1.1.1 Grid Computing
1
1.1.2 Semantic Web
4
1.2 Semantic Grid
7
1.2.1 Basic Concepts
7
1.2.2 Brief History
8
1.3 Basic Issues
9
1.3.1 Knowledge Representation for the Semantic Grid
9
1.3.2 Semantic Data Integration
9
1.3.3 Semantic Service Composition and Process Coordination
10
1.3.4 Semantic Mining and Knowledge Discovery in the Semantic Grid
10
1.3.5 Trust and Security
10
1.4 Case Studies
11
1.4.1 myGrid
11
1.4.2 CombeChem
11
1.4.3 CoAKTinG
12
1.4.4 K-WF Grid
12
1.4.5 Semantic Grid Research and Development in China
12
1.5 Summary and Conclusion
13
References
14
2 Knowledge Representation for the Semantic Grid
15
2.1 Introduction
15
2.2 Knowledge Representation
17
2.2.1 Mathematical Logic
17
2.2.2 Semantic Network
18
2.2.3 Frames
20
2.2.4 Ontology
21
2.3 Description Logic
23
2.4 Knowledge Representation Framework for the Semantic Grid
26
2.4.1 XML and XML Schema
27
2.4.2 RDF and RDF Schema
28
2.4.3 Web Ontology Language
29
2.5 Ontology Development and Application for TCM
32
2.5.1 Ontology Design and Development for UTCMLS
32
2.5.2 TCM Ontology
37
2.6 Summary and Conclusion
45
References
46
3 Dynamic Problem Solving in the Semantic Grid
48
3.1 Introduction
48
3.1.1 Problem Solving
48
3.1.2 Cooperative Distributed Problem Solving
49
3.1.3 Multi-Agent System
50
3.2 Grid-based Problem Solving
51
3.2.1 Grid and Problem Solving
51
3.2.2 Problem Solving in the Semantic Grid
53
3.3 Ontology Management for Grid-based Problem Solving
54
3.3.1 Grid-based Ontology Management
55
3.3.2 Ontology Grid Node
56
3.3.3 Semantic View
59
3.4 Ontology Reuse for Grid-based Problem Solving
61
3.4.1 Dynamic Memory Model
61
3.4.2 Case-based Ontology Repository
63
3.5 Dynamic Problem Solving Based on SubO Evolution
66
3.5.1 Sub-Ontology Manipulations
67
3.5.2 Terminology
69
3.5.3 Problem-Solving Environment
69
3.5.4 Sub-Ontology Based Problem Solving
71
3.6 The Relationship between Problem Solving and the Semantic Grid
73
3.7 Related Works
75
3.8 Summary and Conclusion
76
References
76
4 Trust Computing in the Semantic Grid
79
4.1 Introduction
79
4.2 Trust for the Semantic Grid
80
4.2.1 Characteristic Features of Trust
81
4.2.2 Cost and Utility
82
4.2.3 Distributed vs. Centralized
83
4.2.4 Semantics of Information
83
4.3 Closed Trust Model
86
4.4 Open Trust Model
91
4.5 Experiments
93
4.6 Related Work
98
4.7 Summary and Conclusion
101
References
102
5 Data Integration in the Semantic Grid
103
5.1 Introduction
103
5.1.1 Related Work
104
5.1.2 Preliminaries
106
5.2 Semantic Mapping in the Semantic Grid
108
5.2.1 The Mapping Issue
108
5.2.2 Basic Mapping System
110
5.2.3 Constraint Mapping
110
5.3 Semantic Query Processing in the Semantic Grid
112
5.3.1 Answering Queries Using SHIQ-RDM Views
112
5.3.2 Rewriting SPARQL Queries Using SHIQ-RDM Views
116
5.4 Summary and Conclusion
122
References
123
6 Service Flow Management in the Semantic Grid
126
6.1 Introduction
126
6.2 Research Framework of Service Flow Management
127
6.2.1 Service Matchmaking and Discovery
128
6.2.2 Service Composition
129
6.2.3 Service Composition Verification
130
6.3 Service Matchmaking in DartFlow
131
6.3.1 An Extended Service Model
131
6.3.2 Service Matchmaking
134
6.3.3 Performance Evaluation
139
6.4 Service Composition in DartFlow
141
6.4.1 Service Composition Framework
142
6.4.2 Rules Types and Definitions
144
6.4.3 Automatic Service Composition Based on Rules
147
6.5 Service Flow Verification in DartFlow
148
6.5.1 Overview of π-Calculus
148
6.5.2 Modeling Service Behavior Using π-Calculus
150
6.5.3 Verification of Service Compatibility
152
6.6 Summary and Conclusion
155
References
155
7 Data Mining and Knowledge Discovery in the Semantic Grid
157
7.1 Introduction
157
7.2 Development of KDD System Architecture
159
7.2.1 Single-computer-based Architecture
159
7.2.2 Parallelized Architecture
160
7.2.3 Distributed Architecture
161
7.2.4 Grid-based Architecture
161
7.2.5 A Summary of the Development of KDD System Architecture
165
7.3 Knowledge Discovery Based on the Semantic Grid
165
7.3.1 Virtual Organizations of Knowledge Discovery in the Semantic Grid
165
7.3.2 Architecture and Components of Knowledge Discovery in the Semantic Grid
167
7.3.3 Characteristics of Knowledge Discovery in the Semantic Grid
169
7.4 Drug Community Discovery Utilizing TCM Semantic Grid
171
7.4.1 Semantic Graph Mining Methodology
172
7.4.2 Use Case: TCM Formulae Interpretation and Herb-Drug Interaction Analysis
174
7.5 Summary and Conclusion
176
References
176
8 DartGrid: A Semantic Grid Implementation
179
8.1 Introduction
179
8.2 DartDB—A Semantic Data Integratidn Toolkit
180
8.2.1 Overview
180
8.2.2 System Features
181
8.2.3 System Architecture
182
8.2.4 Mapping from Relational Data to Semantic Web Ontology
183
8.2.5 Semantic Browser and Query Tool
184
8.2.6 Semantic Search Engine
185
8.3 DartFlow—A Service Flow Management Prototype
188
8.3.1 Overview
188
8.3.2 System Architecture
188
8.3.3 Main Functions
190
8.4 Summary and Conclusion
194
9 Semantic Grid Applications for Traditional Chinese Medicine
195
9.1 Background, Status, and Problems of TCM Informatics
195
9.1.1 Background of TCM Informatics
196
9.1.2 Status of TCM Informatics
196
9.1.3 Problems of TCM Informatics
197
9.2 The Architecture of TCM e-Science Semantic Grid
199
9.2.1 Overview
199
9.2.2 Three Layers of TCM e-Science Environment
200
9.2.3 Application Platforms in TCM e-Science Environment
200
9.3 Collaborative TCM Ontology Engineering
202
9.4 Creating a Semantic Grid of TCM Databases
204
9.5 A Semantic Grid Environment for Database Construction
206
9.6 TCM Knowledge Discovery Platform
207
9.7 Summary
209
References
209
10 Semantic Grid Applications in Intelligent Transportation Systems 210
10.1 Introduction
210
10.1.1 ITS System and Grid Computing
211
10.1.2 ITS System and Ontology
213
10.2 Layered Architecture for ITS-Grid
214
10.3 ITS Semantic Grid
215
10.3.1 The Development of an ITS Ontology
215
10.3.2 ITS-Grid Applications
217
10.4 Case Study
222
10.5 Summary and Conclusion
224
References
225
Index 227
Prof. Wus major research areas include distributed artificial intelligence, knowledge-based system, grid computing, semantic web, and ubiquitous computing. He invented the first KB-system tool, ZIPE, based on the C programming language on a SUN platform in China, 1990. He proposed the first coupling knowledge representing model, Couplingua, which embodies Rule, Frame, Semantic Network and Nerve Cell Network and supports symbol computing and traditional data processing computing. During the past few years, his work is mainly focused on semantic web, grid computing, ubiquitous computing and their applications in the life sciences (especially for Traditional Chinese Medicine) and ITS (Intelligent Transportation System). He is the leader of the project DartGrid, a semantic grid toolkit for data integration, which has been used to help build the largest TCM data grid in the world. In year 2005, he was awarded as one of the Outstanding Young Scientists by China National Science Foundation.