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E-grāmata: Service-Oriented Distributed Knowledge Discovery [Taylor & Francis e-book]

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  • Formāts: 230 pages
  • Izdošanas datums: 19-Jun-2019
  • Izdevniecība: Chapman & Hall/CRC
  • ISBN-13: 9780429109911
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Service-Oriented Distributed Knowledge DiscoveryPalielināt
  • Formāts: 230 pages
  • Izdošanas datums: 19-Jun-2019
  • Izdevniecība: Chapman & Hall/CRC
  • ISBN-13: 9780429109911
Citas grāmatas par šo tēmu:
Taylor & Francis e-booksMore info about Taylor & Francis e-books
Rokasgrāmatas mājas lapa: https://www.taylorfrancis.com/books/9780429109911

A new approach to distributed large-scale data mining, service-oriented knowledge discovery extracts useful knowledge from today’s often unmanageable volumes of data by exploiting data mining and machine learning distributed models and techniques in service-oriented infrastructures. Service-Oriented Distributed Knowledge Discovery presents techniques, algorithms, and systems based on the service-oriented paradigm. Through detailed descriptions of real software systems, it shows how the techniques, models, and architectures can be implemented.





The book covers key areas in data mining and service-oriented computing. It presents the concepts and principles of distributed knowledge discovery and service-oriented data mining. The authors illustrate how to design services for data analytics, describe real systems for implementing distributed knowledge discovery applications, and explore mobile data mining models. They also discuss the future role of service-oriented knowledge discovery in ubiquitous discovery processes and large-scale data analytics.





Highlighting the latest achievements in the field, the book gives many examples of the state of the art in service-oriented knowledge discovery. Both novices and more seasoned researchers will learn useful concepts related to distributed data mining and service-oriented data analysis. Developers will also gain insight on how to successfully use service-oriented knowledge discovery in databases (KDD) frameworks.

Preface xv
Acknowledgments xvii
Authors xix
Chapter 1 Distributed Knowledge Discovery: An Overview
1(38)
1.1 Knowledge Discovery And Data Mining Concepts
1(5)
1.1.1 Knowledge Discovery in Databases (KDD) Process Models
4(2)
1.2 Data Mining Techniques
6(16)
1.2.1 Components of Data Mining Algorithms
8(2)
1.2.2 Model Representation
10(1)
1.2.2.1 Decision Trees
10(1)
1.2.2.2 Production Rules
11(1)
1.2.2.3 Decision Lists
12(1)
1.2.2.4 Association Rules
12(1)
1.2.2.5 Neural Networks
13(4)
1.2.2.6 Genetic Algorithms
17(5)
1.3 Parallel Knowledge Discovery
22(11)
1.3.1 Parallelism in Data Mining Techniques
24(1)
1.3.1.1 Parallel Decision Trees
25(1)
1.3.1.2 Parallel Association Rules Discovery
26(1)
1.3.1.3 Parallel Neural Networks
27(2)
1.3.1.4 Parallel Genetic Algorithms
29(1)
1.3.1.5 Parallel Cluster Analysis
30(2)
1.3.1.6 Architectural and Research Issues
32(1)
1.4 Distributed Knowledge Discovery
33(6)
1.4.1 Ensemble Learning
35(1)
1.4.2 Meta-Learning
36(2)
1.4.3 Collective Data Mining
38(1)
Chapter 2 Service-Oriented Computing for Data Analysis
39(28)
2.1 Service-Oriented Architecture And Computing
39(4)
2.2 Internet Services: Web, Grids, And Clouds
43(16)
2.2.1 Web Services
44(2)
2.2.1.1 Protocol Stack
46(2)
2.2.1.2 Messaging Layer. SOAP
48(2)
2.2.1.3 Description Layer: WSDL
50(3)
2.2.2 Grid Services
53(2)
2.2.2.1 Open Grid Services Architecture
55(1)
2.2.2.2 Web Services Resource Framework
56(2)
2.2.3 Cloud Services
58(1)
2.3 Service-Oriented Knowledge Discovery
59(8)
2.3.1 Grid-Based Knowledge Discovery
60(7)
Chapter 3 Designing Services for Distributed Knowledge Discovery
67(14)
3.1 A Service-Oriented Layered Approach For Distributed KDD
67(4)
3.2 How Kdd Applications Can Be Designed As A Collection Of Data Analysis Services
71(2)
3.3 KDD Service-Oriented Applications
73(6)
3.3.1 Example Scenarios
73(3)
3.3.2 Invocation Mechanisms
76(3)
3.4 Hierarchy Of Services For Worldwide KDD
79(2)
Chapter 4 Workflows of Services for Data Analysis
81(18)
4.1 Basic Workflow Concepts
81(5)
4.1.1 Workflow Patterns
82(4)
4.2 Scientific Workflow Management Systems
86(5)
4.2.1 Taverna
86(1)
4.2.2 Triana
87(1)
4.2.3 Pegasus
88(1)
4.2.4 Kepler
89(1)
4.2.5 Askalon
90(1)
4.3 Workflows For Distributed KDD
91(8)
4.3.1 Distributed KDD Workflow Examples
91(2)
4.3.2 Distributed KDD Workflow Representations
93(6)
Chapter 5 Services and Grids: The Knowledge Grid
99(24)
5.1 The Knowledge Grid Architecture
99(3)
5.1.1 Core Services and High-Level Services
100(2)
5.2 Metadata Management
102(5)
5.2.1 Metadata Representation
103(3)
5.2.2 Metadata Publication and Search
106(1)
5.3 Workflow Composition Using Dis3gno
107(8)
5.3.1 Workflow Representation
109(4)
5.3.2 Workflow Composition
113(2)
5.4 Execution Management
115(8)
Chapter 6 Mining Tasks as Services: The Case of Weka4WS
123(22)
6.1 Enabling Distributed KDD In An Open-Source Toolkit
123(3)
6.1.1 Weka: An Overview
124(1)
6.1.2 Weka4WS: Design Goals
125(1)
6.2 Weka4ws Architecture
126(6)
6.2.1 Communication Mechanisms
127(1)
6.2.2 Web Service Operations
128(3)
6.2.3 Client Application
131(1)
6.3 Weka4ws Explorer For Remote Data Mining
132(2)
6.4 Weka4ws Knowledgeflow For Composing Data Mining Services
134(6)
6.4.1 Supporting Data-Parallel Workflows
137(3)
6.5 Execution Management
140(5)
Chapter 7 How Services Can Support Mobile Data Mining
145(18)
7.1 Mobile Data Mining
145(3)
7.1.1 Mobile Data Mining Systems Examples
147(1)
7.2 Mobile Web Services
148(2)
7.2.1 Mobile Web Services Initiatives
149(1)
7.3 System For Mobile Data Mining Through Web Services
150(6)
7.3.1 System Architecture
151(1)
7.3.2 Mining Server Components
152(1)
7.3.3 Mobile Client Components
153(1)
7.3.4 Execution Mechanisms
154(1)
7.3.5 System Implementation
155(1)
7.4 Mobile-To-Mobile (M2M) Data Mining Architecture
156(7)
7.4.1 M2M Data Mining Implementation
158(5)
Chapter 8 Knowledge Discovery Applications
163(18)
8.1 Knowledge Grid Applications
163(7)
8.1.1 Classification with Parameter Sweeping
165(3)
8.1.2 Ensemble Learning
168(2)
8.2 weka4ws Applications
170(5)
8.2.1 Classification Using Multiple Algorithms
171(1)
8.2.2 Clustering with Parameter Sweeping
172(2)
8.2.3 Local Execution on a Multicore Machine
174(1)
8.3 Web Services Resource Framework (Wsrf) Overhead In Distributed Scenarios
175(6)
Chapter 9 Sketching the Future Pervasive Data Services
181(12)
9.1 Service Orientation And Ubiquitous Computing For Data
181(2)
9.2 Toward Future Service-Oriented Infrastructures
183(3)
9.3 Requirements Of Future Generation Services
186(1)
9.4 Services For Ubiquitous Computing
187(2)
9.5 Services For Ambient Intelligence And Smart Territories
189(2)
9.6 Conclusive Remarks
191(2)
Bibliography 193(10)
Index 203
Domenico Talia is a professor of computer engineering at the University of Calabria and the director of the Institute of High Performance Computing and Networking of the Italian National Research Council (ICAR-CNR). Dr. Talia is a member of the Association for Computing Machinery and IEEE Computer Society and an editorial board member of the following journals: IEEE Transactions on Computers, Future Generation Computer Systems, International Journal of Web and Grid Services, Journal of Cloud ComputingAdvances, Systems and Applications, Scalable Computing Practice and Experience, International Journal of Next-Generation Computing, Multiagent and Grid Systems: An International Journal, and Web Intelligence and Agent Systems. His research interests include parallel and distributed data mining algorithms, Cloud computing, Grid services, distributed knowledge discovery, peer-to-peer systems, and parallel programming models.





Paolo Trunfio is an assistant professor of computer engineering at the University of Calabria. He has previously worked at the Swedish Institute of Computer Science (SICS) and the Institute of Systems and Computer Science of the Italian National Research Council (ISI-CNR). Dr. Trunfio is a member of the editorial board of ISRN Artificial Intelligence. His research interests include Grid computing, Cloud computing, service-oriented architectures, distributed knowledge discovery, and peer-to-peer systems.
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