Handbook of big data technologies /
This handbook offers comprehensive coverage of recent advancements in Big Data technologies and related paradigms. Chapters are authored by international leading experts in the field, and have been reviewed and revised for maximum reader value. The volume consists of twenty-five chapters organized i...
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| Online Access: |
Full text (MCPHS users only) |
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| Other Authors: | , |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Cham, Switzerland :
Springer,
2017
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| Subjects: | |
| Local Note: | ProQuest Ebook Central |
Table of Contents:
- Foreword; Preface; Contents; Part I Fundamentals of Big Data Processing; Big Data Storage and Data Models; 1 Storage Models; 1.1 Block-Based Storage; 1.2 File-Based Storage; 1.3 Object-Based Storage; 1.4 Comparison of Storage Models; 2 Data Models; 2.1 NoSQL (Not only SQL); 2.2 Relational-Based; 2.3 Summary of Data Models; References; Big Data Programming Models; 1 MapReduce; 1.1 Features; 1.2 Examples; 2 Functional Programming; 2.1 Features; 2.2 Example Frameworks; 3 SQL-Like; 3.1 Features; 3.2 Examples; 4 Actor Model; 4.1 Features; 4.2 Examples; 5 Statistical and Analytical; 5.1 Features.
- 5.2 Examples6 Dataflow-Based; 6.1 Features; 6.2 Examples; 7 Bulk Synchronous Parallel; 7.1 Features; 7.2 Examples; 8 High Level DSL; 8.1 Pig Latin; 8.2 Crunch/FlumeJava; 8.3 Cascading; 8.4 Dryad LINQ; 8.5 Trident; 8.6 Green Marl; 8.7 Asterix Query Language (AQL); 8.8 IBM Jaql; 9 Discussion and Conclusion; References; Programming Platforms for Big Data Analysis; 1 Introduction; 2 Requirements of Big Data Programming Support; 3 Classification of Programming Platforms; 3.1 Data Source; 3.2 Processing Technique; 4 Major Existing Programming Platforms; 4.1 Data Parallel Programming Platforms.
- 4.2 Graph Parallel Programming Platforms4.3 Task Parallel Platforms; 4.4 Stream Processing Programming Platforms; 5 A Unifying Framework; 5.1 Comparison of Existing Programming Platforms; 5.2 Need for Unifying Framework; 5.3 MatrixMap Framework; 6 Conclusion and Future Directions; References; Big Data Analysis on Clouds; 1 Introduction; 2 Introducing Cloud Computing; 2.1 Basic Concepts; 2.2 Cloud Service Distribution and Deployment Models; 3 Cloud Solutions for Big Data; 3.1 Microsoft Azure; 3.2 Amazon Web Services; 3.3 OpenNebula; 3.4 OpenStack; 4 Systems for Big Data Analytics in the Cloud.
- 4.1 MapReduce4.2 Spark; 4.3 Mahout; 4.4 Hunk; 4.5 Sector/Sphere; 4.6 BigML; 4.7 Kognitio Analytical Platform; 4.8 Data Analysis Workflows; 4.9 NoSQL Models for Data Analytics; 4.10 Visual Analytics; 4.11 Big Data Funding Projects; 4.12 Historical Review; 4.13 Summary; 5 Research Trends; 6 Conclusions; References; Data Organization and Curation in Big Data; 1 Big Data Indexing Techniques; 1.1 Overview; 1.2 Record-Level Non-adaptive Indexing; 1.3 Record-Level Adaptive Indexing; 1.4 Split-Level Indexing; 1.5 Hadoop-RDBMS Hybrid Indexing; 2 Data Organization and Layout Techniques; 2.1 Overview.
- 2.2 Result Materialization and Caching Techniques2.3 Pre-processing and Colocation Techniques; 2.4 None Row-Oriented Storage Layouts; 3 Non-traditional Workloads in Big Data; 3.1 Overview; 3.2 Techniques for Recurring Workloads; 3.3 Techniques for Fast Online Analytics ; 4 Curation and Metadata Management in Big Data; 4.1 Overview; 4.2 Execution-Centric Metadata Approach; 4.3 Provenance-Centric Metadata Approach; 4.4 Data-Centric Metadata Approach; 5 Conclusion; References; Big Data Query Engines; 1 Introduction; 1.1 MPP Query Engines; 1.2 Hadoop Query Engines; 1.3 Chapter Organization.
