results of big data analyses” [10]. Let us note that visualization
in Big Data context is static. Indeed, data are not stored in
a relational way and real-time updates require processing
large amount of data; but this problem has started to be
addressed [3]. Here we present some techniques for Big Data
visualization.9
• Tag Cloud. It is a method for visualizing and linking
concepts of a precise domain or web site. These concepts
are written using text properties such as font size, weight,
or color.
• Clustergram. M. Schonlau [28] defines clustergram as
a visualization technique used for cluster analysis
displaying how individual members of a dataset are
assigned to clusters as the number of clusters increases.
As for every clustering process the number of clusters is
important and it has the advantage to easily perceive how
the number influences partitioning results.
• History Flow. F.B. Viégas, M. Wattenberg and K. Dave [29]
present history flow as a visualization technique designed
to show the evolution of a document efficiently with
respect to the contributions of its different authors. The
horizontal axis of a history flow carries time and the
vertical axis the names of the authors. A color code is
assigned to each author and the vertical length of a bar
indicates the amount of text written by each author.
• Spatial information flow. It is another visualization
technique that represents spatial information flows. It
is mostly represented as a lighting graph where edges
connect sites located on a map.
Visualization can also be used to solve Big Data problems.
For a brief review on this topic, see [30].
3.2. Data analytics
Big Data Analytics can be defined as the use of advanced
analytic techniques on big data [31]. Nowadays, we can put big
data and analytics together. The prior conditions are present
for the development of big data Analytics. First of all, Tools
and storage capabilities can handle big data. Next, by its
size, big data provides large statistical samples and enhanced
results of experiments. Finally, companies and governments
have clearly identified the benefits to develop the economics
of big data. Due to the characteristics of big data, mainly
variety, there are many techniques used for analytics on big
data [32].
• Association rule learning to find relationships among entities
(mainly used in recommendation systems).
• Machine learning to bring computer to learn complex
patterns and make intelligent decisions based on it [10].
• Data mining which can be seen as a combination of statistics
and machine learning and statistics with database
management [10].
• Cluster analysis used as unsupervised machine learning. It
aims to divide data into smaller clusters having the same
set of characteristics not known in advance.