2.2.3: Visual Analytics
Other Information:
The explosion in computing techniques led to the generation of a tremendous amount of data which are stored in the internet
and processed in the IT infrastructures all over the world. Some examples of new technologies for data collections80 are:
web logs; RFID; sensor networks; social networks; social data (due to the Social data revolution), Internet text and documents;
Internet search indexing; call detail records; astronomy, atmospheric science, genomics, biogeochemical, biological; military
surveillance; medical records; photography archives; video archives; large- scale eCommerce. In managing this huge amount
of data, when it comes to human- computer interaction there is a need to distil the most important information to be presented
it in a humanly understandable and comprehensive way. Here it comes visualisation, which is a way to interpret and translate
data from computer understandable formats to human ones by employing graphical models, charts, graphs and other images that
are conventional for humans81. In a sense we can define visualisation as any technique for creating images, diagrams, or animations
to communicate a message or an idea. In contrast with visualisation traditionally seen as the output of the analytical process,
visual analytics considers visualisation as a dynamic tool that aims at integrating the outstanding capabilities of humans
in terms of visual information exploration and the enormous processing power of computers to form a powerful knowledge discovery
environment. In this view visual analytics is useful for tackling the increasing amount of data available, and for using in
the best way the information contained in the data itself. Moreover visual analytics aims at present the data in way suitable
for informing the policy making process. More in particular the interdisciplinary field of visual analytics aims at combining
human perception and computing power in order to solve the information overload problem. In Thomas and Cook' definition82,
visual analytics is "the science of analytical reasoning supported by interactive visual interfaces". Precisely visual analytics
is an iterative process that involves information gathering, data preprocessing, knowledge representation, interaction and
decision making. The characteristic of this filed is that it entails the association of data- mining and text- mining technologies,
used for preprocessing massive amounts of data, and information visualisation 83 , which is useful for disentangling important
from trivial and useless information. In a certain way information visualisation becomes a tool in a semi- automated analytical
process characterized by the cooperation between humans and computers, in which is the user who decides the direction of the
analysis relating to a particular task, while the system works as an interaction tool. It is somehow difficult to distinguish
among information visualisation, scientific visualization84 and visual analytics. In poor terms we can say that scientific
visualisation deals with data having a natural geometric structure, while information visualization handles abstract data
structures such as trees or graphs, and finally visual analytics deals properly with sense- making and reasoning. More in
particular information visualization is mostly applied to data not belonging to scientific inquiry, e.g. graphical representations
of data for business, government, news and social media. Visualization work does not necessarily deal with an analysis task
nor does it always use advanced data analysis algorithms. On the other hand visual analytics can be seen as an integral approach
to decision- making, combining visualization, human factors and data analysis. It entails identifying the best algorithm for
a given analysis task, to be integrated with the best automated analysis algorithms with appropriate visualization and interaction
techniques. Visualization and visual analytics should be considered in strict integration with other research areas, such
as modelling and simulation85, social network analysis, participatory sensing, open linked data, visual computing. The disciplines
in the domain of visualization and visual analytics are: Human- Computer Interaction (HCI), Usability Engineering, Cognitive
and Perceptual Science, Decision Science, Information Visualisation, Scientific Visualisation, Databases, Data Mining, Statistics,
Knowledge Discovery, Data Management & Knowledge Representation, Presentation, Production and Dissemination, Statistics, Interaction,
Geospatial Analytics, Graphics and Rendering, Cognition, Perception, and Interaction. As far the visual analytics methodologies
are concerned, in the CROSSOVER taxonomy we can identify the following: visualisation of a single, static, embedded data set;
visualisation of multiple static data sets; visualisation of a single live data feed or updating data set; and finally visualisation
of multiple data points, including live feeds or updates.
Indicator(s):
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