Anouncia / Gohel / Vairamuthu Data Visualization

Trends and Challenges Toward Multidisciplinary Perception
1. Auflage 2020
ISBN: 978-981-15-2282-6
Verlag: Springer Nature Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 188 Seiten

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.

Autoren/Hrsg.

Anouncia, S. Margret

Gohel, Hardik A.

Vairamuthu, Subbiah

Inhaltsverzeichnis

1;Foreword;5
2;Preface;7
3;Contents;9
4;About the Editors;10
5; Narrative and Text Visualization: A Technique to Enhance Teaching Learning Process in Higher Education;12
5.1;1 Introduction;12
5.2;2 Model for Integrating Narrative and Text Visualization;13
5.3;3 Application of Narrative and Text Visualization;15
5.3.1;3.1 Implementation Narrative Visualization;15
5.3.2;3.2 Implementation of Text Visualization;18
5.3.3;3.3 Integration of Narrative and Text Visualization;21
5.4;4 Conclusion;23
5.5;References;24
6; Data Visualization and Analysis for Air Quality Monitoring Using IBM Watson IoT Platform;25
6.1;1 Big Data Analytics;25
6.1.1;1.1 Applications of Big Data Analytics;26
6.1.2;1.2 Benefits of Big Data Analytics;27
6.1.3;1.3 Challenges of Big Data Analytics;27
6.2;2 Data Visualization;28
6.2.1;2.1 Process;30
6.3;3 Air Quality Index;31
6.4;4 IBM Watson IoT Platform: Turn Numbers into Narratives;34
6.5;5 Visualizing Using IBM Watson IoT Platform;36
6.6;6 Conclusion;42
6.7;References;42
7; Comparative Analysis of Tools for Big Data Visualization and Challenges;43
7.1;1 Introduction;44
7.1.1;1.1 Data Visualization;44
7.1.2;1.2 The Need for Data Visualization;45
7.1.3;1.3 Some Traditional Tools for Data Visualization;46
7.1.4;1.4 The Weaknesses of These Tools;48
7.2;2 Visualizing Big Data;48
7.2.1;2.1 Brief Introduction to Big Data;48
7.2.2;2.2 Characteristics of Big Data;50
7.2.3;2.3 Handling Large Data Volumes;51
7.2.4;2.4 Visualizing Semi-structured and Unstructured Data;53
7.3;3 Visualization Tools for Big Data;55
7.3.1;3.1 Drawbacks of the Traditional Tools;55
7.3.2;3.2 Tools Available for Data Visualization in the Context of Big Data;56
7.3.3;3.3 Technical Competencies of These Tools;57
7.4;4 Challenges;58
7.4.1;4.1 Gaps in Research in Finding Tools for Visualization of Big Data;58
7.5;5 Future Scope;59
7.6;6 Conclusion;59
7.7;References;60
8; Data Visualization Techniques: Traditional Data to Big Data;63
8.1;1 Introduction;63
8.2;2 Importance of Visualization;65
8.3;3 Factors Affecting Data Visualization;65
8.4;4 Traditional Data Visualization Techniques;66
8.4.1;4.1 Line Charts;67
8.4.2;4.2 Pie Charts;67
8.4.3;4.3 Bar Charts;68
8.4.4;4.4 Area Chart;68
8.4.5;4.5 Bubble Chart;69
8.4.6;4.6 Scattered Plot;69
8.4.7;4.7 Tree Maps;70
8.4.8;4.8 Heap Maps;70
8.5;5 Visualizing Big Data—Tools and Techniques;71
8.5.1;5.1 Word Clouds;71
8.5.2;5.2 Symbol Maps;72
8.5.3;5.3 Connectivity Charts;73
8.6;6 Visualization in Agile Software Development;74
8.6.1;6.1 The Portfolio Wall;76
8.6.2;6.2 The Kanban Board;77
8.6.3;6.3 The Burndown Chart;79
8.6.4;6.4 Epic and Story Mapping;81
8.7;7 Challenges of Big Data Visualization;82
8.8;8 Choosing Appropriate Visualization Method;83
8.9;9 Conclusion;83
8.10;10 Summary;84
9; Data Visualization: Visualization of Social Media Marketing Analysis Data to Generate Effective Business Revenue Model;85
9.1;1 Introduction;85
9.2;2 Background;86
9.3;3 Purpose;87
9.4;4 Dataset Description;87
9.5;5 Methodology;88
9.5.1;5.1 Importing the Dataset and Initial Analysis;88
9.5.2;5.2 Constructing the Correlation Matrix and Corrgram;88
9.5.3;5.3 Hypothesis Testing and T-Test;90
9.5.4;5.4 Visualizing the Sales and Marketing Data;94
9.6;6 Issues, Controversies, and Problems;97
9.6.1;6.1 The Issues in Retrieval;97
9.6.2;6.2 Issues in Getting Access to the Marketing Data for Visualization;97
9.6.3;6.3 Controversies Where Sales Data Was Gathered Illegitimately;97
9.7;7 Problems in Prediction and Visualization;98
9.8;8 Solutions;98
9.9;9 Results;99
9.10;10 Future Research Directions;99
9.10.1;10.1 Parallel Coordinates;100
9.10.2;10.2 Alluvial Diagrams;100
9.10.3;10.3 Circle Packing;100
9.11;11 Conclusion;101
9.12;References;102
10; Applications of Visualization Techniques;103
10.1;1 Why Do We Use Data Visualization?;104
10.2;2 Applications of Data Visualization in the Real World;104
10.2.1;2.1 Data Visualization for In-House Communication and Client Reporting—Business Intelligence;104
10.2.2;2.2 Marketing Content and Data Visualization;105
10.2.3;2.3 Data Visualization for Text Mining—Semantic Technology;105
10.2.4;2.4 Collaborative Visual Analysis—Exploring and Making Sense of Data with Others;106
10.3;3 Data Visualization Techniques;106
10.4;4 Case Study;111
10.4.1;4.1 Event Detection Using Social Text Streams with Data Visualization;112
10.4.2;4.2 Political Event Detection from Social Text Streams with Data Visualization;114
10.4.3;4.3 Problem Statement;114
10.4.4;4.4 Solution;115
10.5;5 Conclusion and Insights Obtained;123
11; Evaluation of IoT Data Visualization Tools and Techniques;125
11.1;1 Introduction;126
11.2;2 Internet of Things;127
11.3;3 Data Visualization;128
11.4;4 Internet of Things with Data Visualization;131
11.5;5 Data Visualization Tools and Techniques for IoT;134
11.5.1;5.1 Different Charts for Data Visualization;134
11.5.2;5.2 Tools for Source Credible Data;137
11.5.3;5.3 Tools for Creating Data Visualizations;139
11.5.4;5.4 Platforms, Tools, and Libraries for IoT Data Visualization;146
11.6;6 Conclusion;147
11.7;References;147
12; Data Visualization: Experiment to Impose DDoS Attack and Its Recovery on Software-Defined Networks;150
12.1;1 Introduction;151
12.2;2 Traditional Network;152
12.3;3 SDN Architecture;153
12.4;4 Architecture of Floodlight Controller;153
12.5;5 Methodologies and Tools;154
12.6;6 Implementation of DDoS Attack on SDN and Recovery;156
12.7;7 Result Analysis;162
12.8;8 Conclusion;168
12.9;References;168
13; Data Visualization of Software-Defined Networks During Load Balancing Experiment Using Floodlight Controller;170
13.1;1 Introduction;170
13.2;2 Implementing Floodlight Controller on SDN;171
13.3;3 Developing SDN-Based Scenario;172
13.4;4 Implementing Load Balancing;175
13.5;5 Experimental Issues and Resolution;178
13.6;6 Graph Generation;178
13.7;7 Performance Analysis;183
13.8;8 Conclusion;187
13.9;References;187