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Handbook of Data Intensive Computing

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Cover of 'Handbook of Data Intensive Computing'

Table of Contents

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    Book Overview
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    Chapter 1 High Performance Network Architectures for Data Intensive Computing
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    Chapter 2 Architecting Data-Intensive Software Systems
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    Chapter 3 ECL/HPCC: A Unified Approach to Big Data
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    Chapter 4 Scalable Storage for Data-Intensive Computing
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    Chapter 5 Computation and Storage Trade-Off for Cost-Effectively Storing Scientific Datasets in the Cloud
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    Chapter 6 A Survey of Load Balancing Techniques for Data Intensive Computing
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    Chapter 7 Resource Management for Data Intensive Clouds Through Dynamic Federation: A Game Theoretic Approach
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    Chapter 8 Salt: Scalable Automated Linking Technology for Data-Intensive Computing
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    Chapter 9 Parallel Processing, Multiprocessors and Virtualization in Data-Intensive Computing
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    Chapter 10 Challenges in Data Intensive Analysis at Scientific Experimental User Facilities
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    Chapter 11 Large-Scale Data Analytics Using Ensemble Clustering
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    Chapter 12 Specification of Data Intensive Applications with Data Dependency and Abstract Clocks
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    Chapter 13 Ensemble Feature Ranking Methods for Data Intensive Computing Applications
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    Chapter 14 Record Linkage Methodology and Applications
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    Chapter 15 Semantic Wrapper: Concise Semantic Querying of Legacy Relational Databases
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    Chapter 16 Security in Data Intensive Computing Systems
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    Chapter 17 Data Security and Privacy in Data-Intensive Computing Clusters
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    Chapter 18 Information Security in Large Scale Distributed Systems
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    Chapter 19 Privacy and Security Requirements of Data Intensive Computing in Clouds
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    Chapter 20 On the Processing of Extreme Scale Datasets in the Geosciences
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    Chapter 21 Parallel Earthquake Simulations on Large-Scale Multicore Supercomputers
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    Chapter 22 Data Intensive Computing: A Biomedical Case Study in Gene Selection and Filtering
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    Chapter 23 Design Space Exploration for Efficient Data Intensive Computing on SoCs
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    Chapter 24 Information Quality and Relevance in Large-Scale Social Information Systems
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    Chapter 25 Geospatial Data Management with Terrafly
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    Chapter 26 An Application for Processing Large and Non-Uniform Media Objects on MapReduce-Based Clusters
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    Chapter 27 Feature Selection Algorithms for Mining High Dimensional DNA Microarray Data
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    Chapter 28 Application of Random Matrix Theory to Analyze Biological Data
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    Chapter 29 Keyword Search on Large-Scale Structured, Semi-Structured, and Unstructured Data
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    Chapter 30 A Distributed Publish/Subscribe System for Large Scale Sensor Networks
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Title
Handbook of Data Intensive Computing
Published by
Springer-Verlag New York, January 2011
DOI 10.1007/978-1-4614-1415-5
ISBNs
978-1-4614-1414-8, 978-1-4614-1415-5, 978-1-4899-9919-1
Authors

Furht, Borko, Escalante, Armando

Editors

Borko Furht, Armando Escalante

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 109 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 3%
Japan 1 <1%
France 1 <1%
Brazil 1 <1%
Unknown 103 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 32 29%
Student > Master 25 23%
Researcher 18 17%
Student > Bachelor 8 7%
Student > Doctoral Student 6 6%
Other 9 8%
Unknown 11 10%
Readers by discipline Count As %
Computer Science 61 56%
Engineering 9 8%
Business, Management and Accounting 4 4%
Agricultural and Biological Sciences 4 4%
Economics, Econometrics and Finance 3 3%
Other 14 13%
Unknown 14 13%