301112 - Relational Data Visualisation: Methods, Analysis & Insights

This report provides an overview of relational data visualisation techniques, highlighting their importance in efficient data presentation and analysis. It discusses various visualisation methods like bar charts, pie charts, line plots, histograms, scatter plots, and network diagrams, evaluating their advantages and disadvantages for relational data. The report includes a literature review on applying data visualisation to relational data, emphasising its role in identifying trends and correlations. The conclusion underscores the effectiveness of data visualisation in data analysis, provided the appropriate technique is applied, while acknowledging the challenges posed by complex Big Data scenarios. Desklib offers this and many other resources for students.

Relational Data Visualisation
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Contents
Introduction...........................................................................................................................................2
Relational Database...............................................................................................................................3
Data Visualisation.................................................................................................................................4
Visualisation Techniques for relational data..........................................................................................5
Conclusion...........................................................................................................................................12
References...........................................................................................................................................13

Introduction
Visualisation helps to represent the data in the form of a chart or as an image so that it can be
easily understood. It is very useful in offices as well as industry, as it can present data well.
Data visualisation can be thought of as a visual communication tool. In data visualisation, a
visual representation of the data is created and studied. It can be used to convey the
information with more clarity and better efficiency. It uses the statistics related graphs, some
plots and many other tools. We can use various options like line graph ( if small changes also
need to be observed precisely ), number charts ( if an immediate overview of a data is
required ), pie charts ( if the composition of something is shown in proportion ), gauge charts
( if an immediate trend has to be shown for a single value ) etc. Other data visualisation
techniques are – Charts, contextual details, Heat Map, Dashboard, Gantt, customizes reports
etc.
Relational Database
Relational database is a set of data in which there is a relationship between the various data
items. In such a case, we can have multiple relations, multiple constraints and we can
interpret numerous results from this data. This makes the process complex. We can represent
the relational database by using a 2 dimensional table.
While using the data visualisation for relational data following points must be kept in mind :
knowing the audience, setting the goal, choosing right type of chart, choosing right colours,
handling Big Data, prioritizing using the concept of hierarchy and ordering, utilizing network
diagrams and word clouds, doing comparisons and then explain our point. If the relational
data is visualised properly, then it can eliminate the need of data mining techniques or
complex SQL analysis. It will save a lot of time and effort as well as we can easily identify

the emerging trends from the charts. They help to find relationship as well as correlation
between the data.
To compare the attributes or values, bar charts or pie charts are helpful. For the study of
hierarchies, arc diagrams, matrix or node link visualisation can be used. The Heat map,
Marimekko chart, parallel coordinates plot, radar chart and Venn diagram can be used to find
correlations. The arc diagram, network diagram, bubble chart, non-vibbon chord diagram,
tree diagram, rose diagram, circle diagram can be used to show connections. The database is
represented by graph, then partitioning is done and then visualisation is done followed by
analysis. For the analysis of geographical or temporal events, we can use timeline and maps.
For the analysis of multidimensional data, methods are parallel coordinates, radar / star chart
as well as scatter plot.
Data Visualisation
In case a comparison has to be made between the data sets, then data visualisation comes as a
very useful tool. It gives us a visual picture which helps to analyse data better.
The MS Excel software can be used as data visualisation software. The macros and VBA
features help in this regard to a great extent.
The models like Map Reduce are not most relevant for the analysis of the relational data
( Assunção et al, 2015 ). The relational data visualisation helps in decision making and
support ( Linden et al, 2017 ). The algorithms such as iVAT and asiVAT are used for
visualising cluster’s tendency generally for object data. This method is not relevant for the
relational data as it is not more focussed on visualisation ( Park et al, 2016 ). A tool can be

very helpful to create as well as visualise the semantic annotations on the relational data
( Mazumdar et al, 2016 ).
The data visualisation techniques can be used to study and analyse Online transactions by use
of Big Data ( Prasad et al, 2019 ). A relational database is a structured database and not just a
simple database in which tables represent the data ( McBrien et al, 2018 ). The fast changing
land interests can be visualised using specialised relational data models ( Barry et al, 2017 ).
The visualisation engines act as the solutions for visualising the data ( Kharlamov et al,
2015 ). Visualisation methods can also be applied to the hierarchical biological data
( Kuznetsova, et al 2018 ).
Today, we have many iteration based algorithms which can help in the computation of
intensive tasks and provide almost real time visualisation ( Ibrahim, et al 2017 ).
Representing the relational information in a graphical way makes it easier to analyse as
compared to that represented in a table ( Purchase, et al 2000 ). We can use the complicated
data visualisation technique to show the change propagation data ( Keller, et al 2005 ).
Visualisation Techniques for relational data
The various data visualisation techniques which can be used for relational data are as follows:
Bar Chart – A bar chart is a graphical representation of data in which the values are
represented by the length or height of rectangular bars. There are 2 axis in this – one gives the
measured values and the other gives a category.
Pie Chart – A pie chart is so called, due to its shape. It shows the various parameters in
proportion to each other. We can easily identify the largest and the smallest share just by
looking at the pie chart.

Line Plot
It is used to plot the relation between 2 variables.
Figure 1
Bar Chart
Bar chart is used for comparison of the data.

Figure 2
Pie and Donut Charts
They give a proportional estimate of various components. But if 2 values are comparable, then
it becomes difficult to find which is greater and which is lesser.
Figure 3

Histogram Plot
It represents the continuous variable’s distribution for a given time period. It is mostly used in
machine learning.
Figure 4
Scatter Plot
It is a 2 dimensional plot which represents the joint variation of the 2 variables. It is used to
find the relation or correlation between two variables.

Figure 5
Box and Whisker Plot for Large Data
It graphically displays the 5 statistics ( minimum, lower quartile, median, upper quartile and
maximum value ). It helps to analyse data better.
Figure 6

Word Clouds and Network Diagrams for Unstructured Data
It shows the frequency of a given word of a size in the cloud. It can be used for Big Data that
is not structured.
Figure 7
Network Diagram
It represents the relations in the form of nodes and ties.

Figure 8
Correlation Matrices
It is a table which shows the correlation coefficients between the variables.
Figure 9
Kernel Density Function

For non-parametric data, Kernel Density Function that shows the probability distribution
function of a random variable can be used.
Figure 10
Conclusion
The data visualisation is a technique used to present data in an efficient manner. As the
amount of data is increasing day by day, the Big Data analysis has become a significant field.
If the data is properly presented using the data visualisation techniques, then important results
can be drawn from the charts used. This leads to better and faster data analysis which helps to
save time and effort. In this study, we have first seen the need of data visualisation methods,
then we have seen various advantages and disadvantages. A literature review has been done
on how we can use data visualisation methods for relational data. Various data visualisation
techniques have been studied with diagrams and compared.
Therefore, we find that data visualisation methods are very helpful for data analysis if proper
technique is applied for proper study case. The only disadvantage is that in case of Big Data
analysis, the visualisation may become complex as the relations between the variables
increase.