Handbook of Big Data Technologies

Handbook of Big Data Technologies Front Cover
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2017-03-26
895 pages

Book Description

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 , storage , programming 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 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.

Table of Contents

Part I Fundamentals of Big Data Processing
Big Data Storage and Data Models
1 Storage Models
2 Data Models

Big Data Programming Models
1 MapReduce
2 Functional Programming
3 SQL-Like
4 Actor Model
5 Statistical and Analytical
6 Dataflow-Based
7 Bulk Synchronous Parallel
8 High Level DSL
9 Discussion and Conclusion

Programming Platforms for Big Data Analysis
1 Introduction
2 Requirements of Big Data Programming Support
3 Classification of Programming Platforms
4 Major Existing Programming Platforms
5 A Unifying Framework
6 Conclusion and Future Directions

Big Data Analysis on Clouds
1 Introduction
2 Introducing
3 Cloud Solutions for Big Data
4 Systems for Big Data Analytics in the Cloud
5 Research Trends
6 Conclusions

Data Organization and Curation in Big Data
1 Big Data Indexing Techniques
2 Data Organization and Layout Techniques
3 Non-traditional Workloads in Big Data
4 Curation and Metadata Management in Big Data
5 Conclusion

Big Data Query Engines
1 Introduction
2 Massively Parallel Query Engines
3 Query Engines
4 SQL on Hadoop
5 Query Optimization
6 Query Execution
7 Summary

Large-Scale Data Stream Processing Systems
1 Introduction
2 Programming Models
3 System Support for Distributed Data Streaming
4 Case Study: Stream Processing with Flink
5 Applications, Trends and Open Challenges
6 Conclusions and Outlook

Part II Semantic Big Data Management
Semantic Data Integration
1 An Important Challenge
2 Current State-of-the-Art
3 The Path Forward

Linked Data Management
1 Introduction
2 Background Information
3 Native Linked Data Stores
4 Provenance for Linked Data

Non-native RDF Storage Engines
1 Introduction
2 Storing Linked Data Using Relational Databases
3 No-SQL Stores
4 Massively Parallel Processing for Linked Data

Exploratory Ad-Hoc Analytics for Big Data
1 Exploratory Analytics for Big Data
2 A Top-K Entity Augmentation System
3 DrillBeyond -- Processing Open World SQL
4 Summary and Future Work

Pattern Matching Over Linked Data Streams
1 Overview
2 Linked Data Dissemination System
3 Experimental Evaluation
4 Related Work
5 Summary

Searching the Big Data: Practices and Experiences in Efficiently Querying Knowledge Bases
1 Introduction
2 Background
3 The Framework of Cache-Based Knowledge Base Querying
4 Similar Queries Suggestion
5 Cache Replacement
6 Implementation and Experimental Evaluation
7 Related Work
8 Discussion and Conclusion

Part III Big Graph Analytics
Management and Analysis of Big Graph Data: Current Systems and Open Challenges
1 Introduction
2 Graph Databases
3 Graph Processing
4 Graph Dataflow Systems
5 Gradoop
6 Comparison
7 Current Research and Open Challenges
8 Conclusions and Outlook

Similarity Search in Large-Scale Graph Databases
1 Introduction
2 Preliminaries
3 The Pruning-Verification Framework
4 State-of-the-Art Approaches
5 Future Research Directions
6 Summary

Big-Graphs: Querying, Mining, and Beyond
1 Introduction
2 Graph Data Models
3 Pattern Matching Techniques Over Big-Graphs
4 Mining Techniques Over Big-Graphs
5 Open Problems
6 Conclusions
7 About Authors

Link and Graph Mining in the Big Data Era
1 Introduction
2 Definitions
3 Temporal Evolution
4 Link Prediction
5 Community Detection
6 Graphs in Big Data
7 Weighted Networks
8 Extending Graph Models: Multilayer Networks
9 Open Challenges
10 Conclusions

Granular Social Network: Model and Applications
1 Introduction
2 Preliminaries
3 Literature Review
4 Fuzzy Granular Social Networks (FGSN)
5 Discussions and Conclusions

Part IV Big Data Applications
Big Data, IoT and Semantics
1 Introduction
2 Semantics for Big Data
3 Big Data and Semantics in the Internet of Things
4 Social Mining
5 Graph Mining
6 Big Stream Data Mining
7 Geo-Referenced Data Mining
8 Conclusion

SCADA Systems in the Cloud
1 Introduction
2 Related Work
3 An Overview of SCADA
4 Moving SCADA to the Cloud
5 Conceptual SCADA Cloud Orchestration Framework
6 Results
7 Conclusion

Quantitative Data Analysis in Finance
1 Introduction
2 The Three V's of Big Data in High Frequency Data
3 Data Cleaning, Aggregating and Management
4 Modeling High Frequency Data in Finance
5 Portfolio Selection and Evaluation
6 The Future
7 Conclusion

Emerging Cost Effective Big Data Architectures
1 Introduction
2 Emerging Solutions for Big Data
3 Future Directions
4 Conclusion

Bringing High Performance Computing to Big Data
1 Introduction
2 GPU Acceleration of Alternating Least Squares
3 GPU Acceleration of Singular Value Decomposition
4 Conclusions

Cognitive Computing: Where Big Data Is Driving Us
1 Cognitive Computing: An Alternative Approach for Clear Understanding
2 Big Data Impulsing Cognitive System
3 Traditional Systems versus Cognitive Systems?
4 Data Mining in the Era of Cognitive Systems
5 Design Methods for Cognitive Systems
6 Cognitive Systems
7 The Future of Cognitive Systems
8 Final Remarks

Privacy-Preserving Record Linkage for Big Data: Current Approaches and Research Challenges
1 Introduction
2 Background
3 Privacy Aspects and Techniques for PPRL
4 Scalability Techniques for PPRL
5 Multi-party PPRL
6 Open Challenges
7 Conclusions

Book Details

  • Title: Handbook of Big Data Technologies
  • Length: 895 pages
  • Edition: 1st ed. 2017
  • Language: English
  • Publisher:
  • Publication Date: 2017-03-26
  • ISBN-10: 3319493396
  • ISBN-13: 9783319493398
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