Handbook of big data technologies / Albert Y. Zomaya, Sherif Sakr, editors.
Zomaya, Albert Y., editor.; Sakr, Sherif, 1979- editor.
2017
QA76.9.B45
Available Online

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Title Handbook of big data technologies / Albert Y. Zomaya, Sherif Sakr, editors.
ISBN 9783319493404 (electronic book)
331949340X (electronic book)
9783319493398
3319493396
DOI https://doi.org/10.1007/978-3-319-49340-4
Published Cham, Switzerland : Springer, 2017.
Language English
Description 1 online resource : illustrations.
Item Number 10.1007/978-3-319-49340-4 doi
Call Number QA76.9.B45
Dewey Decimal Classification 005.7
Summary 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 into four main parts. Part one covers the fundamental concepts of Big Data technologies including data curation mechanisms, data models, storage models, programming models and programming platforms. It also dives into the details of implementing Big SQL query engines and big stream processing systems. Part Two focuses on the semantic aspects of Big Data management including data integration and exploratory ad hoc analysis in addition to structured querying and pattern matching techniques. Part Three presents a comprehensive overview of large scale graph processing. It covers the most recent research in large scale graph processing platforms, introducing several scalable graph querying and mining mechanisms in domains such as social networks. Part Four details novel applications that have been made possible by the rapid emergence of Big Data technologies such as Internet-of-Things (IOT), Cognitive Computing and SCADA Systems. All parts of the book discuss open research problems, including potential opportunities, that have arisen from the rapid progress of Big Data technologies and the associated increasing requirements of application domains. Designed for researchers, IT professionals and graduate students, this book is a timely contribution to the growing Big Data field. Big Data has been recognized as one of leading emerging technologies that will have a major contribution and impact on the various fields of science and varies aspect of the human society over the coming decades. Therefore, the content in this book will be an essential tool to help readers understand the development and future of the field.
Access Note Access limited to authorized users.
Digital File Characteristics text file PDF
Source of Description Online resource; title from PDF title page (SpringerLink, viewed March 7, 2017).
Added Author Zomaya, Albert Y., editor.
Sakr, Sherif, 1979- editor.
Available in Other Form Print version: 9783319493398
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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

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