Information or Data Visualizing Techniques

Information or Data Visualizing Techniques is the graphical presentation of information,
with the goal of providing the viewer with a qualitative understanding of the information contents.
Information can be in any form, like data, processes, relations, or concepts. Graphical presentation may entail manipulation of graphical entities (points, lines, shapes, images, text) and attributes
(color, size, position, shape). Understanding of information involves detection, measurement, and comparison. It can be further enhanced via interactive techniques and providing the information
from multiple views and with multiple techniques.

One of the active research and challenging task is representing and making sense of multidimensional data, partially due to the three- dimensional space we live in. Visualization techniques are powerful sense-making tools that support knowledge workers in their decision-making activities by stimulating visual thinking. While most of the scientific, engineering, and business data is multi-dimensional;
i.e. datasets contain typically more than three attributes of data.

Data presentation can be beautiful, elegant and descriptive. There is a variety of conventional ways to visualize data – tables, histograms, pie charts and bar graphs are being used every day, in every project and on every possible occasion. However, to convey a message to your readers effectively, sometimes you need more than just a simple pie chart of your results. In fact, there are much better, profound, creative and absolutely fascinating ways to visualize data. Many of them might become ubiquitous in the next few years.

Characteristics of Data:
• Numeric, symbolic (or mix)
• Scalar, vector, or complex structure
• Various units
• Discrete or continuous
• Spatial, quantity, category, temporal, relational, structural
• Accurate or approximate
• Dense or sparse
• Ordered or non-ordered
• Disjoint or overlapping
• Binary, enumerated, multilevel
• Independent or dependent
• Multidimensional
• Single or multiple sets
• May have similarity or distance metric
• May have intuitive graphical representation (e.g. temperature with color)
• Has semantics which may be crucial in graphical consideration