Service-Oriented Distributed Knowledge Discovery

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ISBN 9781439875315
Cat# K13494



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ISBN 9781439875339
Cat# KE13632



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  • Introduces parallel and distributed data mining concepts and architectures
  • Describes web and grid technologies for distributed knowledge discovery
  • Explains how to design service-oriented data mining applications
  • Includes a study of workflow formalisms for modeling distributed knowledge discovery applications
  • Presents open source frameworks for developing service-oriented KDD applications, with the open source software available on the authors' website


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.

Table of Contents

Distributed Knowledge Discovery: An Overview
Knowledge Discovery and Data Mining Concepts
Data Mining Techniques
Parallel Knowledge Discovery
Distributed Knowledge Discovery

Service-Oriented Computing for Data Analysis
Service-Oriented Architecture and Computing
Internet Services: Web, Grids, and Clouds
Service-Oriented Knowledge Discovery

Designing Services for Distributed Knowledge Discovery
A Service-Oriented Layered Approach for Distributed KDD
How KDD Applications Can Be Designed as a Collection of Data Analysis Services
KDD Service-Oriented Applications
Hierarchy of Services for Worldwide KDD

Workflows of Services for Data Analysis
Basic Workflow Concept
Scientific Workflow Management Systems
Workflows for Distributed KDD

Services and Grids: The Knowledge Grid
The Knowledge Grid Architecture
Metadata Management
Workflow Composition Using DIS3GNO
Execution Management

Mining Tasks as Services: The Case of Weka4WS
Enabling Distributed KDD in an Open-Source Toolkit
Weka4WS Architecture
Weka4WS Explorer for Remote Data Mining
Weka4WS Knowledge Flow for Composing Data Mining Services
Execution Management

How Services Can Support Mobile Data Mining
Mobile Data Mining
Mobile Web Services
System for Mobile Data Mining through Web Services
Mobile-to-Mobile (M2M) Data Mining Architecture

Knowledge Discovery Applications
Knowledge Grid Applications
Weka4WS Applications
Web Services Resource Framework (WSRF) Overhead in Distributed Scenarios

Sketching the Future Pervasive Data Services
Service Orientation and Ubiquitous Computing for Data
Toward Future Service-Oriented Infrastructures
Requirements of Future Generation Services
Services for Ubiquitous Computing
Services for Ambient Intelligence and Smart Territories
Conclusive Remarks



Author Bio(s)