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Data Types
Nominal Equal or not equal than others, order does not matter
Ordinal Order is important, they can form ordered sets or
tuples
Numerical can do arithmetic operations on them spatial, geophysical, temporal
Data Transformations
Involves loss (filter, aggregation) or gain (revealing another aspect) of information
New values can be derived: Mathematical operations: sum Statistical operations: mean
The data type can change: Numerical -->Ordinal : classing Nominal --> Ordinal : sorting
Data Tables
Structured information ready to be mapped to visual structures.
We know : •the number of attributes dimensionality•the types of the attributes
Visual Structures Spatial substrate – perceptually dominant
Main aspect : axes Types of axes: nominal, ordinal, quantitative
Marks(glyphs) 4 elementary types:points, lines, areas, volumes Take up space
Marks’s graphical properties Spatial : size, orientation Object related: Grayscale, Color, Texture, Shape Some are more effective than others
Visual Mapping Example - FilmFinderInteractive controls
•Ratings --> Oy•FilmType->Nx
Scatterplot •Year Qx•Popularity Qy.•FilmIdP(Year, Popularity)•FilmID(FilmType) -> P(Color)
Networks The most complex data structures that need to be
visualized
Used to describe telecom and computer networks, World Wide Web
Contains cycles sole encoding technique : connectivity
Graphs containing just tens of nodes tend to look like a ball of tangled strings map clutter
Important aspects in visualizing networks
Positioning the nodes key to readability Geographical position: SeeNet Graphical layout algorithms : mininization of link crossing Advanced iterative positioning algorithms : spring embedder, planar straight line embedding
Managing the links so they convey information Color the links according to type or utilization: SemNet Select a link subset with the help of interactive sliders: SeeNet Represent a pair of directed arcs as a single arc: SeeNet
Handling large-scale graphs -> uses some form of aggregation Hierarchical clustering : SemNet Geographical clustering: SeeNet Clustering based on natural hierarchy : software systems
Interacting with and navigating through large networks Levels of detail can enable nonuniform and interactively progressive aggregation
Using 3D: Pros and ConsPros: more effective for 3D physical data additional dimension for encoding data can accommodate large-scale visual structures
Cons: greater implementation challenges: shading,
lighting, navigation, occlusion significantly more processing power
View transformations
Location probes Details on demand Brushing Magic lenses
Viewpoint controls Zoom, pan, clip Overview+detail
Distorsions (Focus+Context) Bifocal : Perspective Wall, Document Lens Polyfocal : Table Lens Based on levels of interest: Fish Eye Lens
Levels of Detail
Distorsion
Monofocal Bifocal Polyfocal Fisheye
Representationspace
Distortedspace
Transfer functionTransfer function
Magnification function
Overview+detail vs. Focus+Context
Overview+detail Simple to implement and understand Operators need to shift attention between the two windows Typical zoom: 5-15 scaling to 1000 with intermediate views
Focus+context Harder to implement and understand Effective if critical features remain undistorted Keeps overview and details visible at all times Typical zoom: 2-5
Interaction
Changes the process of understanding dataAllows the user to explore more possibilities in a given timeHas to be sufficiently fast to be efficient
Time and Interaction
Three levels of interaction Finest level – psychological moment – 0.1 s
Stimuli fuse in a single percept Animation breaks down if longer than 10 frames/second.
Intermediate level – unprepared response – 1s The user has a minimum of time to respond
Coarsest level – unit task –about 10 sec Can do a minimal unit of cognitive work
Animation must be slowed down in some cases
Interactive visualization systems need scheduling mechanisms
Interacting with Data Transformations
Dynamic Queries Uses interactive controls to filter data
Direct Walk Navigates from record to record through linking ->Web
browser
Details On Demand
Attribute Walk Searches for objects with similar properties as a selected one
Brushing
Interacting with Visual Mappings
Data FlowUses explicit node-link diagram to represent
the mappings Pivot tables
Lets the user rapidly manipulate the mapping of data to rows and collumns
Interacting with View Transformations
Direct selection
Camera movement
Magic lens
Overview+detail
Zooming
Visualization levels of useInfosphere Information workspace
Visually enhanced objectsVisual knowledge tools
We learned about..
The different phases in the information visualization process
Improving the use of space and displaying more variables
Displaying complex structures such as trees and networks
The main visual transformations Interacting with a visualization system
Bibliography
“Readings in information visualization: Using vision to think”, S.K. Card, J.D. MacKinlay, B. Schneiderman, 1999, Morgan Kaufmann Publishers
“Information Visualization: Beyond the horizon”, 2nd ed, Chaomei Chen, Springer
“Information Visualization”, Robert Spence, 2000, Addison-Wesley
http://www.infovis-wiki.net/ IEEE Information Visualization Conference
http://vis.computer.org/
ATLAS TDAQ system Selects interesting events coming
out of the ATLAS detector: 64 TBytes/s tens of Mytes/s
Uses a three layer trigger architecture
Uses three distinct switched Ethernet networks Data FrontEnd Data BackEnd Control
The three networks total over 4000 ports, 2500 PCs, 200 edge switches and 5 chassis core switches
ATLAS-Specific Visualization Requirements
A comprehensive monitoring system provides large amounts of data -> what data to present to who and how
Three types of consumer groups: Networking experts System analists Operators
The ideal visualization system would: Follow the system architecture and its data flow Display both traffic and status info in real-time Allow efficient navigation so as not to lose the big picture
Available 2D display solutions have proven to be very limited
3D visualization system
Chose 3D because it can cope better with large scale models
Profited from recent advances: levels-of-detail, fly-through navigation, proximity sensors etc
Used X3D standard: Can be extended through prototypes Has an external Scene Access Interface Chosen X3D browser : Octaga Player
Modeled a hierarchical 3D model for the TDAQ system
Hierarchical 3D model
Top layer Overall picture at a glance Contains processor farms and network cores
Middle layer Contains processors and edge switches grouped by
functionality Bottom layer
Contains individual ports and their associated traffic statistics and plots
Port level view
Ox time (last 5 intervals)Oy traffic (top) and errors (bottom)Color traffic state( Critical, Stressed, Normal) Uses both alignment and recursion
Details on Demand (additional port info, traffic plots)
Device level view
Control switch Data switch
Processor
Each device displays:•Traffic panels •Overall status light – its state/color is a result of statistics aggregation
Used different colors to distinguish data and control networks
Farm level view
Each rack has:
•Multiple processors •A data switch (top)•A control switch (bottom)
Due to natural aggregation at the rack level, we were able to use proximity instead of connectivity eliminated map clutter
WALK navigation is used inside a farm
Top level view Uses connectivity to display
the relationships between subsystems
Shape differentiates the subsystems: Boxes Farm processors Cylinders Network Cores
Color differentiates the networks: Yellow : Data Front End Red: Data BackEnd Green:Control
Uses FLY navigation