1.3 Overview 5
Chapter 4 considers data mining, which is seen as fundamental to the automated Kai Puolamäki
Alessio Bertone
Roberto Therón
Otto Huisman
Jimmy Johansson
Silvia Miksch
Panagiotis Papapetrou
Salvo Rinzivillo
analysis components of visual analytics. Since today’s datasets are often
extremely large and complex, the combination of human and automatic analysis
is key to solving many information gathering tasks. Some case studies are
presented which illustrate the use of knowledge discovery and data mining
(KDD) in bioinformatics and climate change. The authors then pose the
question of whether industry is ready for visual analytics, citing examples of
the pharmaceutical, software and marketing industries. The state of the art
section gives a comprehensive review of data mining/analysis tools such as
statistical and mathematical tools, visual data mining tools, Web tools and
packages. Some current data mining/visual analytics approaches are then
described with examples from the bioinformatics and graph visualisation fields.
Technical challenges specific to data mining are described such as achieving
data cleaning, integration, data fusion etc. in real-time and providing the
necessary infrastructure to support data mining. The challenge of integrating
the human into the data process to go towards a visual analytics approach is
discussed together with issues regarding its evaluation. Several opportunities
are then identified, such as the need for generic tools and methods, visualisation
of models and collaboration between the KDD and visualisation communi-
ties.
Chapter 5 describes the requirements of visual analytics for spatio-temporal Gennady Andrienko
Natalia Andrienko
Heidrun Schumann
Christian Tominski
Urska Demsar
Doris Dransch
Jason Dykes
Sara Fabrikan
Mikael Jern
Menno-Jan Kraak
applications. Space (as in for example maps) and time (values change over
time) are essential components of many data analysis problems; hence there is
a strong need for visual analytics tools specifically designed to deal with the par-
ticular characteristics of these dimensions. Using a sizeable fictitious scenario,
the authors guide the reader towards the specifics of time and space, illustrating
the involvement of various people and agencies, and the many dependencies
and problems associated with scale and uncertainties in the data. The current
state of the art is described with a review of maps, geographic information
systems, the representation of time, interactive and collaborative issues, and the
implication of dealing with massive datasets. Challenges are then identified,
such as dealing with diverse data at multiple scales, and supporting a varied set
of users, including non-experts.
Chapter 6 highlights the fact that currently most visual analytics application Jean-Daniel Fekete
are custom-built stand-alone applications, using for instance, in-memory data
storage rather than database management systems. In addition, many other
common components of visual analytics applications can be identified and po-
tentially built into a unifying framework to support a range of applications. The
author of this chapter reviews architectural models of visualisation, data man-
agement, analysis, dissemination and communication components and outlines
the inherent challenges. Opportunities and next steps for current research are
subsequently identified which encourage a collaborative multidisciplinary effort
to provide a much needed flexible infrastructure.
Chapter 7 discusses visual perception and cognitive issues - human aspects Alan Dix
Margit Pohl
Geoffrey Ellis
of visual analytics. Following a review of the psychology of perception
and cognition, distributed cognition, problem solving, particular interaction
issues, the authors suggest that we can learn much from early application