Graphics Reference
In-Depth Information
der into a browser window the Scalable Vector
Graphics files (SVG, an XML-based language for
describing geometric objects).
In philosophy, ontology is a discipline that
studies theories about the nature of existence.
In information science, ontology is a basic part
of the semantic web, a document or file that for-
mally defines terms and relations among terms.
Ontologies collect information about objects,
their classes, properties, relations, and changes in
properties and relations, restrictions, and rules, and
thus help the users understand, exchange, analyze
or share knowledge of a specific domain. Users
can thus understand, exchange, analyze, or share
knowledge of a specific domain. Visualization
of the structure of ontology systems represents
the real world objects. For example, curricular
structures and instances are needed to represent
individual college courses.
A user may feed a semantic web application
with huge collections of data coming from various
sources: collections of text documents (composed
mostly of HTML), music and other audio files,
photos and other images (mostly graphics), web
pages, bookmarks, blog posts, library catalogs
and worldwide directories, syndication and ag-
gregation of news, software, animations, data,
applications, and e-mails or archived e-mail mes-
sages. The semantic web tool distills core concepts
from these files and then finds the relevant new
information. Semantic web involves examining
internal content of the relations in a network of
concepts, and uses words and symbols as labels
for data. Thus, the semantic web comprises phi-
losophy, design principles, collaborative working
groups, and technology (W3C Semantic Web
Activity, 2011).
combines the use of abstract visual metaphors,
mathematical deduction, and human intuitive
interaction to detect patterns within dynamic in-
formation resources and thus gain knowledge and
insight. Huge data sets have millions of records
that come from various different sources and
provide a dynamic, many times inconsistent and
incompatible data. Analytical reasoning allows
finding significant, often unpredicted patterns in
databases.
Visual analytics is a combination of computa-
tional and visual methods in exploration process.
Visual and presentational exploration is shifting
to visual analytic enquiries that aim at broaden-
ing our awareness of knowledge. Researchers
developing techniques beyond visualization to
analyze data visually, simplify the complexities,
reveal uncertainties, and complete incomplete-
ness, advocate a new framework that emerges
both from information-rich disciplines like hu-
manities, psychology, sociology, and business
and the science-rich disciplines (Banissi, 2011).
Integration of interactive visualization tools, data
mining, and statistics enable analytical reason-
ing and collaboration. Visual metaphors, math-
ematical deduction, and human intuitive analysis
detect patterns, and gain knowledge and insight.
Visualization of large amounts of financial data
for investments can support decision making for
investors on the financial market.
As Jern and Franzén (2006) stated, visual
analytics requires interdisciplinary science, go-
ing beyond traditional scientific and information
visualization to include statistics, mathematics,
knowledge representation, management and
discovery technologies, cognitive and perceptual
sciences, decision sciences, and more. By applying
visual analytics people derive insight from data,
ant then gain and communicate assessments for
action. For example, visual analytics may be used
for a study of the entire genome of an organism,
where data and visual representations are analyzed
at many abstraction levels, formats, and scales:
molecules, gene networks, signaling networks,
3.8. Visual Analytics
Visual analytics focuses on handling massive,
dynamic volumes of information through applica-
tion of related research areas including visualiza-
tion, data mining, and statistics. Visual analytics
