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Mastering Data-Intensive Collaboration and Decision Making

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Cover of 'Mastering Data-Intensive Collaboration and Decision Making'

Table of Contents

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    Book Overview
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    Chapter 1 The Dicode Project
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    Chapter 2 Data Intensiveness and Cognitive Complexity in Contemporary Collaboration and Decision Making Settings
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    Chapter 3 Requirements for big data analytics supporting decision making: A sensemaking perspective
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    Chapter 4 Making Sense of Linked Data: A Semantic Exploration Approach
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    Chapter 5 The Dicode Data Mining Services
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    Chapter 6 The Dicode Collaboration and Decision Making Support Services
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    Chapter 7 Integrating Dicode Services: The Dicode Workbench
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    Chapter 8 Clinico-Genomic Research Assimilator: A Dicode Use Case
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    Chapter 9 Opinion Mining from Unstructured Web 2.0 Data: A Dicode Use Case
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    Chapter 10 Data Mining in Data-Intensive and Cognitively-Complex Settings: Lessons Learned from the Dicode Project
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    Chapter 11 Collaboration and Decision Making in Data-Intensive and Cognitively-Complex Settings: Lessons Learned from the Dicode Project
Attention for Chapter 11: Collaboration and Decision Making in Data-Intensive and Cognitively-Complex Settings: Lessons Learned from the Dicode Project
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Citations

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Chapter title
Collaboration and Decision Making in Data-Intensive and Cognitively-Complex Settings: Lessons Learned from the Dicode Project
Chapter number 11
Book title
Mastering Data-Intensive Collaboration and Decision Making
Published by
Springer, Cham, January 2014
DOI 10.1007/978-3-319-02612-1_11
Book ISBNs
978-3-31-902611-4, 978-3-31-902612-1
Authors

Spyros Christodoulou, Manolis Tzagarakis, Nikos Karacapilidis, Fan Yang-Turner, Lydia Lau, Vania Dimitrova

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 33%
Unknown 2 67%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 67%
Student > Doctoral Student 1 33%
Readers by discipline Count As %
Computer Science 3 100%

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