Download Information Encoding

Visualization Encoding
Introduction
 Information visualization starts from data.
 There are many forms that the data could
take, text, spreadsheets, relational DB tuples,
etc.
 There are many patterns that the data could
follow, clustering, outlier, correlation, etc.
 Encoding:
Data
Application
Domain
Graphic
Presentation
Fundamental Tasks
 Information presentation.
Maps, Photographs, Movies, …
 Information extraction.
Interactive graphical interface
Information Presentation
Data Mining Example: Clustering
Information Extraction
Data Mining Example: Clustering
Data Types
 1-D, 2-D, 3-D, temporal, multi-dimensional,
tree and network data.
 Data types characterize the information
objects in the task domain.
Basic Visualization Tasks
 Overview of a collection of data.
 Zoom in/on objects of interest.
 Filter out uninterested items.
 Details-on-demand: view details.
 Relate: View relationship.
 History: Undo, Redo, Refinement.
 Extract a subset of the data.
1-D Data and Task Encoding
 Linear data: textual document, source code,
etc.
 User problems: count, find, replace, …
 Encoding: fonts, color, size, layout,
scrolling, selection capabilities, …
 Product example: text editor, browser, …
2-D Data and Task Encoding
 Planar or map data: geographical maps,
floor plans, newspaper layouts, …
 User problems: find adjacent items, search
containment, find paths, filtering, detailson-demand, …
 Encoding: size, color, layout, arrangement,
multiple layers, …
 Product example: CAD
3-D Data and Task Encoding
 Real-world objects: building, human body
 User problems: adjacency in 3-D,
inside/outside relationship, position,
orientation, occlusion
 Encoding: overviews, landmarks,
transparency, color, perspective, stereo
display
 Product example: CAD
Temporal Data and Task
Encoding
 Time series data: medical records, project
management, historical presentation
 User problems: finding all events before,
after or during some time period or moment.
 Encoding: time lines
Multi-dimensional Data and Task
Encoding
 Relational and statistical databases tuples.
 User problem: finding patterns, clusters,
correlations, gaps, outliers.
 Challenge:
– Simultaneously display many dimensions of
large subsets of data.
– Create displays that best encode the data pattern
for a particular task.
– Rapidly select a subset of tuples or dimensions.
An Encoding Example
Dimensionality Encoding
 Multi-dimensional databases are structured
as n-dimensional data cube.
 The dimensions of the data can be explicitly
encoded in the structure of tables.
Data Set Encoding
 The data sources are encoded as layers.
 The different result sets are encoded as
different panes in different layers.
User Interest Encoding
 Providing enough tools and allowing user to
specify his interest.
 The table configuration encodes the user
interest.
 Table configurations are defined in form of
algebra
– Concatenation
– Cross product
– Nest (Division)
 For ordinal fields, algebra operand symbols
take all domain values.
– A = domain (A) = {a1, a2, …, an}
– Example: Month = {Jan, Feb, …, Dec}
 For quantitative fields, algebra operand
symbols take the field names as values.
– P = {P}
– Example: Profit = {Profit}
 Ordinal fields partition the table into rows
and columns; quantitative fields are
spatially encoded as axes within the panes.
 Concatenation Example:
– Quarter = {Qtr1, Qtr2, Qtr2, Qtr4}
– Product = {Coffee, Espresso, Herbal, Tea}
– Profit = {Profit}, Sales = {Sales}
Ordinal Field
Quantitative Field
Group By
Sorted By
 Cross Product Example:
– Ordinal x Ordinal
 Ordinal x Quantitative
 Nest (Division) Example:
 Quantitative field does not make sense for
divisions
Product x SumOfSales
Quarter x SumOfProfit
Types of Graphics inside Panes
 Types of Panes:
– Ordinal – Ordinal
– Ordinal – Quantitative
– Quantitative - Quantitative
Visual Encoding
 Shape
 Size
 Orientation
 Color
Tree Type Data and Task
Encoding
 Exponential data: hierarchies, tree
structures.
 User problems: find the structural properties
– Height of the tree
– Number of children
– Find nodes with same attributes
 Encoding:
– Outline style of indented labels
 Node-link diagrams: allowing the encoding of
linkage between entities.
 Treemap: child rectangles inside parent
rectangles
 Product example: windows explorer, internet
traffic, hyperbolic browser
Network Data and Task Encoding
 Graph data: multiple paths, cycles, lattices
 User problems:
– Shortest path
– Topology problems
 Encoding: imperfect
– Node-link diagram
– Matrix
General Encoding Principles
 Expressiveness:
– Encode all the facts in the result set.
– Encode only the facts in the result set.
 Effectiveness:
– Depends on the capability of the perceiver.
– Encode the more important information more
effectively.
– Perceptual accuracy ranks
Conclusion
 Visualization helps
– Information presentation
– Information extraction
 Good visual encoding should match the
target data and user problems.
 Studying the successful/unsuccessful visual
encoding designs and techniques helps us to
design and develop new encoding
approaches.