Data Visualization (eBook)

Trends and Challenges Toward Multidisciplinary Perception

S. Margret Anouncia, Hardik A. Gohel, Subbiah Vairamuthu (Herausgeber)

eBook Download: PDF

2020 | 1st ed. 2020
XII, 179 Seiten
Springer Singapore (Verlag)
978-981-15-2282-6 (ISBN)

Infos

This book discusses the recent trends and developments in the fields of information processing and information visualization. In view of the increasing amount of data, there is a need to develop visualization techniques to make that data easily understandable. Presenting such approaches from various disciplines, this book serves as a useful resource for graduates.

Dr Margret Anouncia S is a Professor at Vellore Institute of Technology (VIT) University in India. She received her bachelor's degree in Computer Science and Engineering from Bharathidasan University (1993), Tiruchirappalli, India, and master's degree in Software Engineering from Anna University (2000), Chennai, India. She was awarded a doctorate in Computer Science and Engineering at VIT University (2008). Her main areas of interest include digital image processing, software engineering, and knowledge engineering. She is the lead author of more than 60 publications in technical journals and proceedings of national and international conferences.

 

Dr Hardik Gohel is an assistant professor in computer science and program director of computer information system at University of Houston - Victoria, TX, USA. Postdoctoral Fellow at the Applied Research Center of Florida International University. He worked as a postdoc fellow in cybersecurity and data science at Florida International University - Miami, FL, USA, from 2016-2019. He holds a Ph.D. in Computer Science, awarded in 2015. Dr. Gohel has extensive research experience in artificial intelligence and his research projects have involved cyber test automation and monitoring, smart bandages for wound monitoring, bigdata for security intelligence, trustworthy cyberspace for security and privacy of social media, predictive maintenance for nuclear infrastructure, and database and mobile forensics infrastructure. Dr. Gohel is also working on how to prepare quality diversified workforce with artificial intelligence in science, technology, engineering and mathematics (STEM) education.

Dr S Vairamuthu is an Associate Professor at Vellore Institute of Technology (VIT) University in India. He received bachelor's and master's degrees in Computer Science and Engineering from Ayya Nadar Janaki Ammal College (Autonomous), affiliated to Madurai Kamaraj University. He is the lead author of more than 20 publications in technical journals and proceedings of national and international conferences. His main areas of interest include human-computer interaction, recommendation systems, machine learning, big data and IoT, and computational intelligence.

Inhalt

Foreword 5
Preface 7
Contents 9
About the Editors 10
Narrative and Text Visualization: A Technique to Enhance Teaching Learning Process in Higher Education 12
1 Introduction 12
2 Model for Integrating Narrative and Text Visualization 13
3 Application of Narrative and Text Visualization 15
3.1 Implementation Narrative Visualization 15
3.2 Implementation of Text Visualization 18
3.3 Integration of Narrative and Text Visualization 21
4 Conclusion 23
References 24
Data Visualization and Analysis for Air Quality Monitoring Using IBM Watson IoT Platform 25
1 Big Data Analytics 25
1.1 Applications of Big Data Analytics 26
1.2 Benefits of Big Data Analytics 27
1.3 Challenges of Big Data Analytics 27
2 Data Visualization 28
2.1 Process 30
3 Air Quality Index 31
4 IBM Watson IoT Platform: Turn Numbers into Narratives 34
5 Visualizing Using IBM Watson IoT Platform 36
6 Conclusion 42
References 42
Comparative Analysis of Tools for Big Data Visualization and Challenges 43
1 Introduction 44
1.1 Data Visualization 44
1.2 The Need for Data Visualization 45
1.3 Some Traditional Tools for Data Visualization 46
1.4 The Weaknesses of These Tools 48
2 Visualizing Big Data 48
2.1 Brief Introduction to Big Data 48
2.2 Characteristics of Big Data 50
2.3 Handling Large Data Volumes 51
2.4 Visualizing Semi-structured and Unstructured Data 53
3 Visualization Tools for Big Data 55
3.1 Drawbacks of the Traditional Tools 55
3.2 Tools Available for Data Visualization in the Context of Big Data 56
3.3 Technical Competencies of These Tools 57
4 Challenges 58
4.1 Gaps in Research in Finding Tools for Visualization of Big Data 58
5 Future Scope 59
6 Conclusion 59
References 60
Data Visualization Techniques: Traditional Data to Big Data 63
1 Introduction 63
2 Importance of Visualization 65
3 Factors Affecting Data Visualization 65
4 Traditional Data Visualization Techniques 66
4.1 Line Charts 67
4.2 Pie Charts 67
4.3 Bar Charts 68
4.4 Area Chart 68
4.5 Bubble Chart 69
4.6 Scattered Plot 69
4.7 Tree Maps 70
4.8 Heap Maps 70
5 Visualizing Big Data—Tools and Techniques 71
5.1 Word Clouds 71
5.2 Symbol Maps 72
5.3 Connectivity Charts 73
6 Visualization in Agile Software Development 74
6.1 The Portfolio Wall 76
6.2 The Kanban Board 77
6.3 The Burndown Chart 79
6.4 Epic and Story Mapping 81
7 Challenges of Big Data Visualization 82
8 Choosing Appropriate Visualization Method 83
9 Conclusion 83
10 Summary 84
Data Visualization: Visualization of Social Media Marketing Analysis Data to Generate Effective Business Revenue Model 85
1 Introduction 85
2 Background 86
3 Purpose 87
4 Dataset Description 87
5 Methodology 88
5.1 Importing the Dataset and Initial Analysis 88
5.2 Constructing the Correlation Matrix and Corrgram 88
5.3 Hypothesis Testing and T-Test 90
5.4 Visualizing the Sales and Marketing Data 94
6 Issues, Controversies, and Problems 97
6.1 The Issues in Retrieval 97
6.2 Issues in Getting Access to the Marketing Data for Visualization 97
6.3 Controversies Where Sales Data Was Gathered Illegitimately 97
7 Problems in Prediction and Visualization 98
8 Solutions 98
9 Results 99
10 Future Research Directions 99
10.1 Parallel Coordinates 100
10.2 Alluvial Diagrams 100
10.3 Circle Packing 100
11 Conclusion 101
References 102
Applications of Visualization Techniques 103
1 Why Do We Use Data Visualization? 104
2 Applications of Data Visualization in the Real World 104
2.1 Data Visualization for In-House Communication and Client Reporting—Business Intelligence 104
2.2 Marketing Content and Data Visualization 105
2.3 Data Visualization for Text Mining—Semantic Technology 105
2.4 Collaborative Visual Analysis—Exploring and Making Sense of Data with Others 106
3 Data Visualization Techniques 106
4 Case Study 111
4.1 Event Detection Using Social Text Streams with Data Visualization 112
4.2 Political Event Detection from Social Text Streams with Data Visualization 114
4.3 Problem Statement 114
4.4 Solution 115
5 Conclusion and Insights Obtained 123
Evaluation of IoT Data Visualization Tools and Techniques 125
1 Introduction 126
2 Internet of Things 127
3 Data Visualization 128
4 Internet of Things with Data Visualization 131
5 Data Visualization Tools and Techniques for IoT 134
5.1 Different Charts for Data Visualization 134
5.2 Tools for Source Credible Data 137
5.3 Tools for Creating Data Visualizations 139
5.4 Platforms, Tools, and Libraries for IoT Data Visualization 146
6 Conclusion 147
References 147
Data Visualization: Experiment to Impose DDoS Attack and Its Recovery on Software-Defined Networks 150
1 Introduction 151
2 Traditional Network 152
3 SDN Architecture 153
4 Architecture of Floodlight Controller 153
5 Methodologies and Tools 154
6 Implementation of DDoS Attack on SDN and Recovery 156
7 Result Analysis 162
8 Conclusion 168
References 168
Data Visualization of Software-Defined Networks During Load Balancing Experiment Using Floodlight Controller 170
1 Introduction 170
2 Implementing Floodlight Controller on SDN 171
3 Developing SDN-Based Scenario 172
4 Implementing Load Balancing 175
5 Experimental Issues and Resolution 178
6 Graph Generation 178
7 Performance Analysis 183
8 Conclusion 187
References 187

Produkt-Details

• Erscheint lt. Verlag: 3.3.2020
• Zusatzinfo: XII, 179 p. 142 illus., 126 illus. in color.
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Datenbanken
• Themenwelt: Informatik ► Netzwerke ► Sicherheit / Firewall
• Themenwelt: Informatik ► Office Programme ► Outlook
• Themenwelt: Informatik ► Theorie / Studium ► Algorithmen
• Themenwelt: Sozialwissenschaften ► Politik / Verwaltung ► Staat / Verwaltung
• Schlagworte: Big Data • data structures • Data Visualization • Decision Making • machine learning • multidimensional data
• ISBN-10: 981-15-2282-0 / 9811522820
• ISBN-13: 978-981-15-2282-6 / 9789811522826