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Handbook of Big Data Technologies

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Cover of 'Handbook of Big Data Technologies'

Table of Contents

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    Book Overview
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    Chapter 1 Big Data Storage and Data Models
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    Chapter 2 Big Data Programming Models
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    Chapter 3 Programming Platforms for Big Data Analysis
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    Chapter 4 Big Data Analysis on Clouds
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    Chapter 5 Data Organization and Curation in Big Data
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    Chapter 6 Big Data Query Engines
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    Chapter 7 Large-Scale Data Stream Processing Systems
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    Chapter 8 Semantic Data Integration
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    Chapter 9 Linked Data Management
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    Chapter 10 Non-native RDF Storage Engines
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    Chapter 11 Exploratory Ad-Hoc Analytics for Big Data
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    Chapter 12 Pattern Matching Over Linked Data Streams
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    Chapter 13 Searching the Big Data: Practices and Experiences in Efficiently Querying Knowledge Bases
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    Chapter 14 Management and Analysis of Big Graph Data: Current Systems and Open Challenges
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    Chapter 15 Similarity Search in Large-Scale Graph Databases
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    Chapter 16 Big-Graphs: Querying, Mining, and Beyond
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    Chapter 17 Link and Graph Mining in the Big Data Era
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    Chapter 18 Granular Social Network: Model and Applications
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    Chapter 19 Big Data, IoT and Semantics
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    Chapter 20 SCADA Systems in the Cloud
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    Chapter 21 Quantitative Data Analysis in Finance
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    Chapter 22 Emerging Cost Effective Big Data Architectures
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    Chapter 23 Bringing High Performance Computing to Big Data Algorithms
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    Chapter 24 Cognitive Computing: Where Big Data Is Driving Us
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    Chapter 25 Privacy-Preserving Record Linkage for Big Data: Current Approaches and Research Challenges
Attention for Chapter 3: Programming Platforms for Big Data Analysis
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Chapter title
Programming Platforms for Big Data Analysis
Chapter number 3
Book title
Handbook of Big Data Technologies
Published by
Springer International Publishing, February 2017
DOI 10.1007/978-3-319-49340-4_3
Book ISBNs
978-3-31-949339-8, 978-3-31-949340-4

Jiannong Cao, Shailey Chawla, Yuqi Wang, Hanqing Wu


Albert Y. Zomaya, Sherif Sakr

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status Count As %
Student > Doctoral Student 2 29%
Student > Master 1 14%
Lecturer > Senior Lecturer 1 14%
Researcher 1 14%
Lecturer 1 14%
Other 1 14%
Readers by discipline Count As %
Computer Science 3 43%
Unspecified 2 29%
Business, Management and Accounting 1 14%
Economics, Econometrics and Finance 1 14%