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Big Data Technologies and Applications.

By: Furht, Borko.
Contributor(s): Villanustre, Flavio.
Material type: TextTextSeries: eBooks on Demand.Publisher: Cham : Springer International Publishing, 2016Description: 1 online resource (405 p.).ISBN: 9783319445502.Subject(s): Big dataGenre/Form: Electronic books.Additional physical formats: Print version:: Big Data Technologies and ApplicationsDDC classification: 5.7 LOC classification: QA76.9.B45.F874 2016Online resources: Click here to view this ebook.
Contents:
Preface -- Acknowledgments -- Contents -- About the Authors -- Big Data Technologies -- 1 Introduction to Big Data -- Concept of Big Data -- Big Data Workflow -- Big Data Technologies -- Big Data Layered Architecture -- Big Data Software -- Hadoop (Apache Foundation) -- Splunk -- LexisNexis' High-Performance Computer Cluster (HPCC) -- Big Data Analytics Techniques -- Clustering Algorithms for Big Data -- Big Data Growth -- Big Data Industries -- Challenges and Opportunities with Big Data -- References -- 2 Big Data Analytics -- Introduction -- Data Analytics -- Data Input -- Data Analysis
Output the Result -- Summary -- Big Data Analytics -- Big Data Input -- Big Data Analysis Frameworks and Platforms -- Researches in Frameworks and Platforms -- Comparison Between the Frameworks/Platforms of Big Data -- Big Data Analysis Algorithms -- Mining Algorithms for Specific Problem -- Machine Learning for Big Data Mining -- Output the Result of Big Data Analysis -- Summary of Process of Big Data Analytics -- The Open Issues -- Platform and Framework Perspective -- Input and Output Ratio of Platform -- Communication Between Systems -- Bottlenecks on Data Analytics System -- Security Issues
Data Mining Perspective -- Data Mining Algorithm for Map-Reduce Solution -- Noise, Outliers, Incomplete and Inconsistent Data -- Bottlenecks on Data Mining Algorithm -- Privacy Issues -- Conclusions -- Acknowledgments -- References -- 3 Transfer Learning Techniques -- Introduction -- Definitions of Transfer Learning -- Homogeneous Transfer Learning -- Instance-Based Transfer Learning -- Asymmetric Feature-Based Transfer Learning -- Symmetric Feature-Based Transfer Learning -- Parameter-Based Transfer Learning -- Relational-Based Transfer Learning
Hybrid-Based (Instance and Parameter) Transfer Learning -- Discussion of Homogeneous Transfer Learning -- Heterogeneous Transfer Learning -- Symmetric Feature-Based Transfer Learning -- Asymmetric Feature-Based Transfer Learning -- Improvements to Heterogeneous Solutions -- Experiment Results -- Discussion of Heterogeneous Solutions -- Negative Transfer -- Transfer Learning Applications -- Conclusion and Discussion -- Appendix -- References -- 4 Visualizing Big Data -- Introduction -- Big Data: An Overview -- Big Data Processing Methods -- Big Data Challenges -- Visualization Methods
Integration with Augmented and Virtual Reality -- Future Research Agenda and Data Visualization Challenges -- Conclusion -- References -- 5 Deep Learning Techniques in Big Data Analytics -- Introduction -- Deep Learning in Data Mining and Machine Learning -- Big Data Analytics -- Applications of Deep Learning in Big Data Analytics -- Semantic Indexing -- Discriminative Tasks and Semantic Tagging -- Deep Learning Challenges in Big Data Analytics -- Incremental Learning for Non-stationary Data -- High-Dimensional Data -- Large-Scale Models -- Future Work on Deep Learning in Big Data Analytics
Conclusion
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Item type Current location Call number URL Status Date due Barcode
Electronic Book UT Tyler Online
Online
QA76.9.B45.F874 2016 (Browse shelf) http://uttyler.eblib.com/patron/FullRecord.aspx?p=4690714 Available EBL4690714

Preface -- Acknowledgments -- Contents -- About the Authors -- Big Data Technologies -- 1 Introduction to Big Data -- Concept of Big Data -- Big Data Workflow -- Big Data Technologies -- Big Data Layered Architecture -- Big Data Software -- Hadoop (Apache Foundation) -- Splunk -- LexisNexis' High-Performance Computer Cluster (HPCC) -- Big Data Analytics Techniques -- Clustering Algorithms for Big Data -- Big Data Growth -- Big Data Industries -- Challenges and Opportunities with Big Data -- References -- 2 Big Data Analytics -- Introduction -- Data Analytics -- Data Input -- Data Analysis

Output the Result -- Summary -- Big Data Analytics -- Big Data Input -- Big Data Analysis Frameworks and Platforms -- Researches in Frameworks and Platforms -- Comparison Between the Frameworks/Platforms of Big Data -- Big Data Analysis Algorithms -- Mining Algorithms for Specific Problem -- Machine Learning for Big Data Mining -- Output the Result of Big Data Analysis -- Summary of Process of Big Data Analytics -- The Open Issues -- Platform and Framework Perspective -- Input and Output Ratio of Platform -- Communication Between Systems -- Bottlenecks on Data Analytics System -- Security Issues

Data Mining Perspective -- Data Mining Algorithm for Map-Reduce Solution -- Noise, Outliers, Incomplete and Inconsistent Data -- Bottlenecks on Data Mining Algorithm -- Privacy Issues -- Conclusions -- Acknowledgments -- References -- 3 Transfer Learning Techniques -- Introduction -- Definitions of Transfer Learning -- Homogeneous Transfer Learning -- Instance-Based Transfer Learning -- Asymmetric Feature-Based Transfer Learning -- Symmetric Feature-Based Transfer Learning -- Parameter-Based Transfer Learning -- Relational-Based Transfer Learning

Hybrid-Based (Instance and Parameter) Transfer Learning -- Discussion of Homogeneous Transfer Learning -- Heterogeneous Transfer Learning -- Symmetric Feature-Based Transfer Learning -- Asymmetric Feature-Based Transfer Learning -- Improvements to Heterogeneous Solutions -- Experiment Results -- Discussion of Heterogeneous Solutions -- Negative Transfer -- Transfer Learning Applications -- Conclusion and Discussion -- Appendix -- References -- 4 Visualizing Big Data -- Introduction -- Big Data: An Overview -- Big Data Processing Methods -- Big Data Challenges -- Visualization Methods

Integration with Augmented and Virtual Reality -- Future Research Agenda and Data Visualization Challenges -- Conclusion -- References -- 5 Deep Learning Techniques in Big Data Analytics -- Introduction -- Deep Learning in Data Mining and Machine Learning -- Big Data Analytics -- Applications of Deep Learning in Big Data Analytics -- Semantic Indexing -- Discriminative Tasks and Semantic Tagging -- Deep Learning Challenges in Big Data Analytics -- Incremental Learning for Non-stationary Data -- High-Dimensional Data -- Large-Scale Models -- Future Work on Deep Learning in Big Data Analytics

Conclusion

Description based upon print version of record.

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