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Data visualization involves presenting data in graphical or pictorial form which makes the information easy to understand. It helps to explain facts and determine courses of action. It will benefit any field of study that requires innovative ways of presenting large, complex information. The advent of computer graphics has shaped modern visualization. This paper presents a brief introduction to data visualization.
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International Journal of Engineering Research And Advanced Technology(IJERAT)
ISSN: 2454-6135 [Volume. 02 Issue.12, December– 2016]
www.sretechjournal.org
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Publication Impact Factor (PIF): 1.02 www.sretechjournal.org
DATA VISUALIZATION
Matthew N. O. Sadiku
1
, Adebowale E. Shadare
2
, Sarhan M. Musa
3
and
Cajetan M. Akujuobi
4
3,2,1
Roy G. Perry College of Engineering, Prairie View A&M University
Prairie View, TX 77446
USA
4
Office of the Vice President for Research and Dean of Graduate School,
Prairie View A&M University
Prairie View, TX 77446
USA
ABSTRACT
Data visualization involves presenting data in graphical or pictorial form which makes the information
easy to understand. It helps to explain facts and determine courses of action. It will benefit any field of
study that requires innovative ways of presenting large, complex information. The advent of computer
graphics has shaped modern visualization. This paper presents a brief introduction to data visualization.
Keywords-
Data visualization, Information Visualization, Scientific Visualization, Big data.
I. INTRODUCTION
There has been the need for displaying massive amounts of data in a way that is easily accessible and
understandable. Organizations generate data every day. As a result, the amount of data available on the
Web has increased dramatically. It is difficult for users to visualize, explore, and use this enormous data.
The ability to visualize data is crucial to scientific research. Today, computers can be used to process
large amounts of data. Data visualization is concerned with the design, development, and application of
computer generated graphical representation of the data. It provides effective data representation of data
originating from different sources. This enables decision makers to see analytics in visual form and
makes it easy for them to make sense of the data. It helps them discover patterns, comprehend
information, and form an opinion.
Data visualization is also regarded as information visualization or scientific visualization. Human
beings have always employed visualizations to make messages or information last in time. What cannot
be touched, smelled or tasted can be represented visually [1].
Adebowale E. Shadare et al., DATA VISUALIZATION
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II. VISUALIZATION TECHNIQUES
Visualization is the use of computer-supported, visual representation of data. Unlike static data
visualization, interactive data visualization allows users to specify the format used in displaying data.
Common visualization techniques are as shown in Figure 1 and include [2]:
Line graph: This shows the relationship between items. It can be used to compare changes over a
period of time.
Bar chart: This is used to compare quantities of different categories.
Scatter plot: This is a two-dimensional plot showing variation of two items.
Pie chart: This is used to compare the parts of a whole.
Thus, the format of graphs and charts can take the form of bar chart, pie chart, line graph, etc. It is
important to understand which chart or graph to use for your data.
Data visualization uses computer graphics to show patterns, trends, and relationship among elements
of the data. It can generate pie charts, bar charts, scatter plots, and other types of data graphs with simple
pull-down menus and mouse clicks. Colors are carefully selected for certain types of visualization. When
color is used to represent data, we must choose effective colors to differentiate between data elements.
In data visualization, data is abstracted and summarized. Spatial variables such as position, size, and
shape represent key elements in the data. A visualization system should perform a data reduction,
transform and project the original dataset on a screen.
It should visualize results in the form of charts and graphs and present results in user friendly way.
Figure 1. Commonly used data visualization techniques.
Data Visualization
Pie
Chart
Line Graph
Scatter Plot
Bar
Chart
International Journal of Engineering Research And Advanced Technology(IJERAT)
ISSN: 2454-6135 [Volume. 02 Issue.12, December– 2016]
www.sretechjournal.org
13
Publication Impact Factor (PIF): 1.02 www.sretechjournal.org
III. APPLICATIONS
Most visualization designs are to aid decision making and serve as tools that augment cognition. In
designing and building a data visualization prototype, one must be guided by how the visualization will
be applied. Data visualization is more than just representing numbers; it involves selecting and
rethinking the numbers on which the visualization is based [3].
Visualization of data is an important branch of computer science and has wide range of application
areas. Several application-specific tools have been developed to analyze individual datasets in many
fields of medicine and science.
Public Health: The ability to analyze and present data in an understandable manner is critical to the
success of public health surveillance. Health researchers need useful and intelligent tools to aid their
work [4]. Security is important in cloud-based medical data visualizations. Open any medical or health
magazine today, and you will see all kinds of graphical representations.
Renewal Energy: Calculation of energy consumption compared to production is important for optimum
solution [5].
Environmental Science: As environmental managers are required to make decisions based on highly
complex data, they require visualization. Visualization applications within applied environmental
research are beginning to emerge [6]. It is desirable to have at one’s disposal different programs for
displaying results.
Fraud Detection: Data visualization is important in the early stages of fraud investigation. Fraud
investigator may use data visualization as a proactive detection approach, using it to see patterns that
suggest fraudulent activity [7].
Library-Decision Making: Data visualization software allows librarians the flexibility to better manage
and present information collected from different sources. It gives them the skill to present information in
a creative, compelling way [8]. Visualization of library data highlights purchasing decisions, future
library needs and goals. Librarians, as de facto experts of data visualization, can assist students, faculty
and researchers visualize their data [9].
Several information visualization algorithms and associated software have been developed. These
software enable users to interpret data more rapidly than ever before. These include ManyEyes from
IBM, SmartMoney for stock market, Insights from Facebook Corporation, Visual Analytics from SAS,
Adebowale E. Shadare et al., DATA VISUALIZATION
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and Thoth from California Institute of Technology, Tableau, and TOPCAT [10, 11]. They make data
visualizations easy to interpret and rapid to produce. Each tool has its own good features and limitations.
Visualization of a large-scale multidimensional data sets can be combined with new approaches of
interacting with a computer using the Web application (as a service).
IV. CHALLENGES
Large, time-varying datasets pose great challenge for data visualization because of the enormous data
volume. Real-time data visualization can enable users to proactively respond to issues that arise.
Animation generation approach is used for interactive exploration process of time-varying data. It
visualizes temporal events by mimicking the composition of storytelling techniques [12].
Users differ in their ability to use data visualization and make decisions under tight time constraints. It
is hard to quantify the merit of a data visualization technique. This is the reason for having a multitude of
visualization algorithms and associated software. Most of these software have not taken advantage of the
multi-touch interactions and direct manipulation capabilities of the new devices.
Big data, structured and unstructured, introduces a unique set of challenges for developing
visualizations. This is due to the fact that we must take into account the speed, size, and diversity of the
data. A new set of issues related to performance, operability, and degree of discrimination challenge
large data visualization and analysis [13]. It is difficult and time-consuming to create a large simulated
data set. It is also difficult to decide what visual might be the best to use.
V. CONCLUSION
Data visualization is the process of representing data in a graphical or pictorial way in a clear and
effective manner. It has emerged as a powerful and widely applicable tool for analyzing and interpreting
large and complex data. It has become a quick, easy means of conveying concepts in a universal format.
It must communicate complex ideas with clarity, accuracy, and efficiency. These benefits have allowed
data visualization to be useful in many fields of study.
International Journal of Engineering Research And Advanced Technology(IJERAT)
ISSN: 2454-6135 [Volume. 02 Issue.12, December– 2016]
www.sretechjournal.org
15
Publication Impact Factor (PIF): 1.02 www.sretechjournal.org
REFERENCES
[1] J. L. V. Sancho, J. C. Dominguez, and B. E. M. Ochoa, “An approach to the taxonomy of data
visualization,” Revista Latina de Communicacion Social, vol. 69, 2014, pp. 486-507.
[2] “Data visualization techniques,” SAS, http://www.sas.com/en_us/offers/sem/data-visualization-
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goCbutACFcolgQodqwgIiA
[3] J. Wolfe, “Teaching students to focus on the data in data visualization,” Journal of Business and
Technical Communication, vol. 29, no. 3, 2015, pp. 344-359.
[4] T. Kilimba, G. Nimako, and K. Herbst, “Data everywhere: an integrated longitudinal data
visualization platform for health and demographic surveillance sites,” Proceedings of the 6th ACM
Conference on Bioinformatics, Computational Biology and Health Informatics, Sept. 2015, pp. 551,552.
[5] O. Kumar and A. Goyal, “Visualization: a novel approach for big data analytics,” Proceedings of the
Second International Conference on Computational Intelligence & Communication Technology, 2016,
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[6] S. Grainger, F. Mao, and W. Buytaert, “Environmental data visualization for non-scientific contexts:
Literature review and design framework,” Environmental Modelling and Software, vol. 85, 2016, pp.
299-318.
[7] W. N. Dilla, R. L. Raschke, “Data visualization for fraud detection: Practice implications and a call
for future research,” International Journal of Accounting Information Systems, vol. 16, 2015, pp. 1-22.
[8] S. A. Murhy, “Data visualization and rapid analytics: applying tableau desktop to support library
decision-making,” Journal of Web Librarianship, vol. 7, no. 4, 2013, pp. 465-476.
[9] T. J. Brigham, “Feast for the eyes: an introduction to data visualization,” Medical Reference Services
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[10] C. Chen, “Information visualization,” WIREs Computational Statistics, vol. 2, July/August, 2010,
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[11] R. R. Laher, “Thoth: software for data visualization and statistics,” Astronomy and Computing, vol.
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[12] L. Yu et al., “Automatic animation for time-varying data visualization,” Pacific Graphics, vol. 29,
no. 7, 2010, pp. 2271 -2280.
[13] X. Li et al., “Advanced aggregate computation for large data visualization,” Proceedings of IEEE
Symposium on Large Data Analysis and Visualization, 2015, pp. 137,138.
Adebowale E. Shadare et al., DATA VISUALIZATION
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ABOUT THE AUTHORS
Matthew N.O. Sadiku, is a professor in the Department of Electrical and Computer
Engineering at Prairie View A&M University, Texas. He is the author of several books and papers. He is
a fellow of IEEE.
Adebowale Shadare, is a doctoral student at Prairie View A&M University, Texas. He is the
author of several papers.
Sarhan M. Musa, is a professor in the Department of Engineering Technology at Prairie View
A&M University, Texas. He has been the director of Prairie View Networking Academy, Texas, since
2004. He is an LTD Spring and Boeing Welliver Fellow.
Cajetan M. Akujuobi, is the vice-president for research and dean of graduate school at Prairie
View A&M University, Texas. He is the author of two books and several papers.
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