Handbook of Data Intensive Computing

Data Intensive Computing refers to capturing, managing, analyzing, and understanding data at volumes and rates that push the frontiers of current technologies. The challenge of data intensive computing is to provide the hardware architectures and related software systems and techniques which are cap...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Furht, Borko (Editor ), Escalante, Armando (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: New York, NY : Springer New York : Imprint: Springer, 2011.
Edición:1st ed. 2011.
Temas:
Database management.
Information retrieval.
Computer architecture.
Business information services.
Computers, Special purpose.
Computer systems.
Application software.
Database Management.
Data Storage Representation.
IT in Business.
Special Purpose and Application-Based Systems.
Computer System Implementation.
Computer and Information Systems Applications.
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • PART I ARCHITECTURES AND SYSTEMS
  • High Performance Network Architectures for Data Intensive Computing
  • Architecting Data-Intensive Software Systems
  • ECL: A High-Level Programming Language for Data-Intensive Supercomputing
  • Scalable Storage for Data-Intensive Computing
  • Computation and Storage Trade-off for Cost-Effective Storage of Scientific Datasets in the Cloud
  • PART II TECHNOLOGIES AND TECHNIQUES
  • Load Balancing Techniques for Data Intensive Computing
  • Resource Management for Data Intensive Clouds Through Dynamic Federation: A Game Theoretic Approach
  • SALT: Scalable Automated Linking Technology for Data Intensive Computing
  • Parallel Processing, Multiprocessors and Virtualization in Data-Intensive Computing
  • Challenges in Data Intensive Analysis and Visualization at Scientific Experimental User Facilities
  • Large-Scale Data Analytics Using Ensemble Clustering
  • Specification of Data Intensive Applications with Data Dependency and Abstract Clocks
  • Ensemble Feature Ranking Methods for Data Intensive Computing Applications
  • Record Linkage Methodology and Applications
  • Semantic Wrapper: Concise Semantic Querying of Legacy Relational Databases
  • PART III SECURITY
  • Security in Data Intensive Computing Systems
  • Data Security and Privacy in Data-Intensive Supercomputing Clusters
  • Information Security in Large Scale Distributed Systems
  • Privacy and Security Requirements of Data Intensive Applications in Clouds
  • PART IV APPLICATIONS
  • On the Processing of Extreme Scale Datasets in the Geosciences
  • Parallel Earthquake Simulations on Large-scale Multicore Supercomputers
  • Data Intensive Computing in Bioinformatics: A Biomedical Case Study in Gene Selection and Filtering
  • Design Space Exploration for Efficient Data Intensive Computing on SoCs
  • Discovering Relevant Entities in Large-scale Social Information Systems.-Geospatial Data Management with Terrafly
  • An Application for Processing Large and Non-uniform Media Objects on MapReduce-based Clusters
  • Feature Selection Algorithms for Mining High-Dimensional DNA Microarray Data
  • Application of Random Matrix Theory to Analyze Biological Data
  • Keyword Search on Large Relational Databases: an OLAP-Oriented Approach
  • A Distributed Publish/Subscribe System for Large Scale Sensor Networks. .

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