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Wisdom Web of Things

Overview of attention for book
Cover of 'Wisdom Web of Things'

Table of Contents

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    Book Overview
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    Chapter 1 Research Challenges and Perspectives on Wisdom Web of Things (W2T)
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    Chapter 2 WaaS—Wisdom as a Service
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    Chapter 3 Towards W2T Foundations: Interactive Granular Computing and Adaptive Judgement
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    Chapter 4 Towards a Situation-Aware Architecture for the Wisdom Web of Things
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    Chapter 5 Context-Awareness in Autonomic Communication and in Accessing Web Information: Issues and Challenges
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    Chapter 6 Ontology-Based Model for Mining User’s Emotions on the Wisdom Web
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    Chapter 7 Multi-level Big Data Content Services for Mental Health Care
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    Chapter 8 Leveraging Neurodata to Support Web User Behavior Analysis
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    Chapter 9 W2T Framework Based U-Pillbox System Towards U-Healthcare for the Elderly
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    Chapter 10 Hot Topic Detection in News Blog Based on W2T Methodology
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    Chapter 11 Attention Inspired Resource Allocation for Heterogeneous Sensors in Internet of Things
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    Chapter 12 Mining Multiplex Structural Patterns from Complex Networks
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    Chapter 13 Suitable Route Recommendation Inspired by Cognition
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    Chapter 14 A Monitoring System for the Safety of Building Structure Based on W2T Methodology
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    Chapter 15 Brain Big Data in Wisdom Web of Things
Attention for Chapter 12: Mining Multiplex Structural Patterns from Complex Networks
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Citations

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Chapter title
Mining Multiplex Structural Patterns from Complex Networks
Chapter number 12
Book title
Wisdom Web of Things
Published by
Springer International Publishing, November 2016
DOI 10.1007/978-3-319-44198-6_12
Book ISBNs
978-3-31-944196-2, 978-3-31-944198-6
Authors

Bo Yang, Jiming Liu

Editors

Ning Zhong, Jianhua Ma, Jiming Liu, Runhe Huang, Xiaohui Tao

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Professor > Associate Professor 1 100%
Readers by discipline Count As %
Computer Science 1 100%

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