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Big Data in Engineering Applications

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Cover of 'Big Data in Engineering Applications'

Table of Contents

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    Book Overview
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    Chapter 1 Applying Big Data Concepts to Improve Flat Steel Production Processes
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    Chapter 2 Parallel Generation of Very High Resolution Digital Elevation Models: High-Performance Computing for Big Spatial Data Analysis
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    Chapter 3 Big-Data Analysis of Process Performance: A Case Study of Smart Cities
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    Chapter 4 Implementing Scalable Machine Learning Algorithms for Mining Big Data: A State-of-the-Art Survey
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    Chapter 5 Concepts of HBase Archetypes in Big Data Engineering
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    Chapter 6 Scalable Framework for Cyber Threat Situational Awareness Based on Domain Name Systems Data Analysis
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    Chapter 7 Big Data in HealthCare
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    Chapter 8 Facing Up to Nomophobia: A Systematic Review of Mobile Phone Apps that Reduce Smartphone Usage
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    Chapter 9 A Fast DBSCAN Algorithm with Spark Implementation
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    Chapter 10 Understanding How Big Data Leads to Social Networking Vulnerability
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    Chapter 11 Big Data Applications in Health Care and Education
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    Chapter 12 BWT: An Index Structure to Speed-Up Both Exact and Inexact String Matching
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    Chapter 13 Traffic Condition Monitoring Using Social Media Analytics
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    Chapter 14 Modelling of Pile Drivability Using Soft Computing Methods
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    Chapter 15 Three Different Adaptive Neuro Fuzzy Computing Techniques for Forecasting Long-Period Daily Streamflows
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    Chapter 16 Prediction of Compressive Strength of Geopolymers Using Multi-objective Feature Selection
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    Chapter 17 Application of Big Data Analysis to Operation of Smart Power Systems
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    Chapter 18 A Structural Graph-Coupled Advanced Machine Learning Ensemble Model for Disease Risk Prediction in a Telehealthcare Environment
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Title
Big Data in Engineering Applications
Published by
Springer Singapore, June 2018
DOI 10.1007/978-981-10-8476-8
ISBNs
978-9-81-108475-1, 978-9-81-108476-8
Editors

Roy, Sanjiban Sekhar, Samui, Pijush, Deo, Ravinesh, Ntalampiras, Stavros

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X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 67 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 67 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 18%
Student > Master 12 18%
Student > Doctoral Student 6 9%
Student > Bachelor 5 7%
Lecturer 5 7%
Other 9 13%
Unknown 18 27%
Readers by discipline Count As %
Engineering 19 28%
Computer Science 17 25%
Social Sciences 2 3%
Earth and Planetary Sciences 2 3%
Biochemistry, Genetics and Molecular Biology 1 1%
Other 4 6%
Unknown 22 33%