Service-Oriented Distributed Knowledge Discovery (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)

Service-Oriented Distributed Knowledge Discovery (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)

By: Domenico Talia (author), Paolo Trunfio (author)Hardback

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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.

About Author

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.


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 Bibliography Index

Product Details

  • ISBN13: 9781439875315
  • Format: Hardback
  • Number Of Pages: 230
  • ID: 9781439875315
  • weight: 521
  • ISBN10: 1439875316

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