Relational Data Visualisation

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