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000779801 24500 $$aHandbook of big data technologies /$$cAlbert Y. Zomaya, Sherif Sakr, editors.
000779801 264_1 $$aCham, Switzerland :$$bSpringer,$$c2017.
000779801 300__ $$a1 online resource :$$billustrations.
000779801 336__ $$atext$$btxt$$2rdacontent
000779801 337__ $$acomputer$$bc$$2rdamedia
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000779801 5050_ $$aForeword; 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
000779801 5058_ $$a5.2 Examples6 Dataflow-Based; 6.1 Features; 6.2 Examples; 7 Bulk Synchronous Parallel; 7.1 Features; 7.2 Examples; 8 High Level DSL; 8.1 Pig Latin; 8.2 Crunch/FlumeJava; 8.3 Cascading; 8.4 Dryad LINQ; 8.5 Trident; 8.6 Green Marl; 8.7 Asterix Query Language (AQL); 8.8 IBM Jaql; 9 Discussion and Conclusion; References; Programming Platforms for Big Data Analysis; 1 Introduction; 2 Requirements of Big Data Programming Support; 3 Classification of Programming Platforms; 3.1 Data Source; 3.2 Processing Technique; 4 Major Existing Programming Platforms; 4.1 Data Parallel Programming Platforms
000779801 5058_ $$a4.2 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
000779801 5058_ $$a4.1 MapReduce4.2 Spark; 4.3 Mahout; 4.4 Hunk; 4.5 Sector/Sphere; 4.6 BigML; 4.7 Kognitio Analytical Platform; 4.8 Data Analysis Workflows; 4.9 NoSQL Models for Data Analytics; 4.10 Visual Analytics; 4.11 Big Data Funding Projects; 4.12 Historical Review; 4.13 Summary; 5 Research Trends; 6 Conclusions; References; Data Organization and Curation in Big Data; 1 Big Data Indexing Techniques; 1.1 Overview; 1.2 Record-Level Non-adaptive Indexing; 1.3 Record-Level Adaptive Indexing; 1.4 Split-Level Indexing; 1.5 Hadoop-RDBMS Hybrid Indexing; 2 Data Organization and Layout Techniques; 2.1 Overview
000779801 5058_ $$a2.2 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
000779801 506__ $$aAccess limited to authorized users.
000779801 520__ $$aThis 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.
000779801 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed March 7, 2017).
000779801 650_0 $$aBig data$$vHandbooks, manuals, etc.
000779801 650_0 $$aDatabase management$$vHandbooks, manuals, etc.
000779801 7001_ $$aZomaya, Albert Y.,$$eeditor.
000779801 7001_ $$aSakr, Sherif,$$d1979-$$eeditor.
000779801 77608 $$iPrint version:$$z3319493396$$z9783319493398$$w(OCoLC)960837938
000779801 852__ $$bebk
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