visualization schemas for flexible information visualization
DESCRIPTION
Visualization Schemas for Flexible Information Visualization. Chris North, Nathan Conklin, Varun Saini Proceedings of IEEE Symposium on InforVis’02 Presented by Mei Huang, Chunyuan Liao Apr. 21,2005. Outline. Relational Data Schema Motivation Related Work Snap-Together Datacompass - PowerPoint PPT PresentationTRANSCRIPT
Visualization Schemas for Flexible Information Visualization
Chris North, Nathan Conklin, Varun SainiProceedings of IEEE Symposium on InforVis’02
Presented by Mei Huang, Chunyuan Liao
Apr. 21,2005
Outline
Relational Data Schema Motivation Related Work Snap-Together Datacompass Summary & Remarks
Relational Data Schema Structural description of data sets Primitives: attributes, tuples and relations
Motivation
Relational data schema enables flexible database design
No corresponding flexible ways to construct effective UI and visualization-- unique data schema unique
visualization/coordination
-- database keeps changing
-- different views for same data
Mismatch in design capabilities
Relational Databases
Traditional Visualization
Design Goal Data design Visualization design
Design Method Data schema Program code
Designer Data owner Programmer only
Design Change Rapid, dynamic Slow, static
Adaptability Flexible Brittle
Related Work
Single relation visualization APT Sage/SageBrush DEVise
Multiple relation visualization Visage DataSplash/Tioga-2 Rivet/Polaris Sieve
DEVise
http://www.cs.wisc.edu/~devise/devise/quick_intro/index.html
Visage
www-2.cs.cmu.edu/~sage/visage.html
DataSplash/Tioga-2
http://datasplash.cs.berkeley.edu/tour_quick.html
Polaris
http://graphics.stanford.edu/projects/polaris/
Snap-Together -- Overview
A strong analogy between relational database concepts and Snap visualization concepts enables a matching level of design capability.
Demo
Snap-Together -- Theory(1)
Snap Visualization Model Multiple views/Components
Schema primitives
Data-centric coordination and Joins
Snap-Together -- Theory(2) Self Join
eg: TreeView [URLs] Table view
Single Joineg: TreeView [URLs] [HitCounts] Scatterplot
Compound Join
eg: TreeView [URLs] [Hits] [Referrers] TableView
Multiple alternative joineg:
TreeView [URLs] [Hits] [Referrers] TableView
TreeView [URLs] [Links] [Referrers] TableView
url_id
url_id refer_id
url_id
url_id
refer_id
refer_id
Snap-Together -- User Interface
Visualization Schemas
-- represented as a graph and support direct manipulation Nodes
-- represent instantiated visualization components Edges
-- represent coordinations between visualizations
Snap-Together -- System Architecture
Theory UI ArchitectureCoordinated
Muti-views
Visualization Model -Visualization
-Coordination
Relational Model
- Relation
- Association
Data
SourceRelationalDatabase
RelationalDatabase
CoordinationManager
CoordinationGraph
VisualizationSchema
DatabaseSchema
DatabaseManager
Snap server
Event-based coordination Send -> Translate -> Receive
-> selection/navigate Extendable architecture
DataCompass
For novice users or very complex database schemas
One-step construction Interchangeable with
visualization schema Bottom-up approach vs.
Top down approach
Summary-- Snap’s three perspectives
Theory: multi-view visualization, coordinating between data design and visualization design
UI: diagrammatic UI to enable rapid customization of visualization without programming
System Architecture: web-based component architecture to support run-time integration of diverse data sources and visualization tools, and dissemination of custom visualization as web pages.
Remarks Merits:
A cohesive and extensible architecture for coordinating visualization components
Flexible and easy user interface, no programming needed
Shortcoming: No support for visual query No integration between query and visualization
schema Limited support for coordinated data navigation
( pan, zoom … )
Thanks!