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Digital Transformation

Overview of attention for book
Cover of 'Digital Transformation'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Engineering Digital Twins and Digital Shadows as Key Enablers for Industry 4.0
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    Chapter 2 Designing Strongly-decoupled Industry 4.0 Applications Across the Stack: A Use Case
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    Chapter 3 Variability in Products and Production
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    Chapter 4 Reference Architectures for Closing the IT/OT Gap
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    Chapter 5 Edge Computing: Use Cases and Research Challenges
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    Chapter 6 Dynamic Access Control in Industry 4.0 Systems
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    Chapter 7 Challenges in OT Security and Their Impacts on Safety-Related Cyber-Physical Production Systems
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    Chapter 8 Runtime Monitoring for Systems of System
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    Chapter 9 Blockchain Technologies in the Design and Operation of Cyber-Physical Systems
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    Chapter 10 Big Data Integration for Industry 4.0
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    Chapter 11 Massive Data Sets – Is Data Quality Still an Issue?
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    Chapter 12 Modelling the Top Floor: Internal and External Data Integration and Exchange
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    Chapter 13 Conceptualizing Analytics: An Overview of Business Intelligence and Analytics from a Conceptual-Modeling Perspective
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    Chapter 14 Discovering Actionable Knowledge for Industry 4.0: From Data Mining to Predictive and Prescriptive Analytics
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    Chapter 15 Process Mining—Discovery, Conformance, and Enhancement of Manufacturing Processes
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    Chapter 16 Symbolic Artificial Intelligence Methods for Prescriptive Analytics
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    Chapter 17 Machine Learning for Cyber-Physical Systems
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    Chapter 18 Visual Data Science for Industrial Applications
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    Chapter 19 Self-Adaptive Digital Assistance Systems for Work 4.0
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    Chapter 20 Digital Transformation—Towards Flexible Human-Centric Enterprises
Attention for Chapter 10: Big Data Integration for Industry 4.0
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Chapter title
Big Data Integration for Industry 4.0
Chapter number 10
Book title
Digital Transformation
Published by
Springer Vieweg, Berlin, Heidelberg, January 2023
DOI 10.1007/978-3-662-65004-2_10
Book ISBNs
978-3-66-265003-5, 978-3-66-265004-2
Authors

Obraczka, Daniel, Saeedi, Alieh, Christen, Victor, Rahm, Erhard

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X Demographics

The data shown below were collected from the profiles of 2 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 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 3 23%
Student > Master 2 15%
Researcher 2 15%
Student > Ph. D. Student 1 8%
Student > Bachelor 1 8%
Other 0 0%
Unknown 4 31%
Readers by discipline Count As %
Unspecified 3 23%
Computer Science 3 23%
Environmental Science 1 8%
Business, Management and Accounting 1 8%
Energy 1 8%
Other 0 0%
Unknown 4 31%