Ch 14: Embed Focus+Context Papers: TreeJuxtaposer Tamara Munzner - - PowerPoint PPT Presentation

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Ch 14: Embed Focus+Context Papers: TreeJuxtaposer Tamara Munzner - - PowerPoint PPT Presentation

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Ch 14: Embed Focus+Context Papers: TreeJuxtaposer Tamara Munzner Department of Computer Science University of British Columbia CPSC 547, Information Visualization Day 14: 5 November 2015 http://www.cs.ubc.ca/~tmm/courses/547-15 News

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http://www.cs.ubc.ca/~tmm/courses/547-15

Ch 14: Embed Focus+Context Papers: TreeJuxtaposer

Tamara Munzner Department of Computer Science University of British Columbia

CPSC 547, Information Visualization Day 14: 5 November 2015

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News

  • reminder: proposals due by Mon 5pm

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Embed: Focus+Context

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  • combine information within

single view

  • elide

– selectively filter and aggregate

  • superimpose layer

– local lens

  • distortion design choices

– region shape: radial, rectilinear, complex – how many regions: one, many – region extent: local, global – interaction metaphor

Embed Elide Data Superimpose Layer Distort Geometry

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Idiom: DOITrees Revisited

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  • elide

– some items dynamically filtered out – some items dynamically aggregated together – some items shown in detail

[DOITrees Revisited: Scalable, Space-Constrained Visualization of Hierarchical Data. Heer and Card. Proc. Advanced Visual Interfaces (AVI), pp. 421–424, 2004.]

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Idiom: Fisheye Lens

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  • distort geometry

– shape: radial – focus: single extent – extent: local – metaphor: draggable lens

http://tulip.labri.fr/TulipDrupal/?q=node/351 http://tulip.labri.fr/TulipDrupal/?q=node/371

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Idiom: Stretch and Squish Navigation

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  • distort geometry

– shape: rectilinear – foci: multiple – impact: global – metaphor: stretch and squish, borders fixed

[TreeJuxtaposer: Scalable Tree Comparison Using Focus+Context With Guaranteed

  • Visibility. Munzner, Guimbretiere,

Tasiran, Zhang, and Zhou. ACM Transactions on Graphics (Proc. SIGGRAPH) 22:3 (2003), 453– 462.]

System: TreeJuxtaposer

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Distortion costs and benefits

  • benefits

– combine focus and context information in single view

  • costs

– length comparisons impaired

  • network/tree topology

comparisons unaffected: connection, containment

– effects of distortion unclear if

  • riginal structure unfamiliar

– object constancy/tracking maybe impaired

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[Living Flows: Enhanced Exploration of Edge-Bundled Graphs Based on GPU-Intensive Edge Rendering. Lambert, Auber, and Melançon. Proc. Intl. Conf. Information Visualisation (IV), pp. 523–530, 2010.]

fisheye lens magnifying lens neighborhood layering Bring and Go

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Further reading

  • Visualization Analysis and Design. Munzner. AK Peters / CRC Press, Oct 2014.

– Chap 14: Embed: Focus+Context

  • A Review of Overview+Detail, Zooming, and Focus+Context Interfaces. Cockburn,

Karlson, and Bederson. ACM Computing Surveys 41:1 (2008), 1–31.

  • A Guide to

Visual Multi-Level Interface Design From Synthesis of Empirical Study

  • Evidence. Lam and Munzner. Synthesis Lectures on

Visualization Series, Morgan Claypool, 2010.

  • Hierarchical Aggregation for Information

Visualization: Overview, Techniques and Design Guidelines. Elmqvist and Fekete. IEEE Transactions on Visualization and Computer Graphics 16:3 (2010), 439–454.

  • A Fisheye Follow-up: Further Reflection on Focus + Context. Furnas. Proc. ACM
  • Conf. Human Factors in Computing Systems (CHI), pp. 999–1008, 2006.

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TreeJuxtaposer video

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[TreeJuxtaposer: Scalable Tree Comparison using Focus+Context with Guaranteed Visibility. Munzner, Guimbretière, Tasiran, Zhang, Zhou. Proc. SIGGRAPH 2003.]

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What and why: Data and task abstraction

  • data: trees

– phylogenetic tree reconstruction

  • siblings unordered, interior nodes inferred
  • task: compare topological structure

– larger query scopes require more explicit tool support

  • compare several is more difficult than

identify/inspect one

– even trickier: summarize all

  • derived data: structural differences

– best corresponding node in other tree

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Tables Dataset Types

Trees able

Actions Query Identify Compare Summarise Network Data Topology

Paths

Targets

Derive

Why? How? What?

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How: Idiom design decisions

  • juxtapose linked views

– show two tree layouts side by side – linked navigation

  • encode with color: linked highlighting

– structural differences – corresponding subtree (click select) – best corresponding node (hover select)

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Facet Juxtapose

Select Manipulate

Facet Juxtapose and Coordinate Views Share Encoding: Same/Different Share Data: All/Subset/None Share Navigation

Linked Highlighting Why? How? What?

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How: Idiom design decisions

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  • embed focus+context in single view

– reduce with complex combination of filtering and aggregation

  • distort geometry

– metaphor: stretch and squish navigation – shape: rectilinear – foci: multiple – impact: global

Distort Geometry

Reduce Filter Aggregate Embed

Why? How? What?

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Algorithm: Stretch and squish navigation

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  • guaranteed visibility of semantically

important marks even when squished small

– TJ: scalability to 500K nodes

  • all preprocessing subquadratic
  • all realtime rendering sublinear
  • guaranteed visibility

– marks always visible – easy with small datasets

algorithm idiom abstraction domain

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Guaranteed visibility challenges

  • hard with larger datasets
  • reasons a mark could be invisible

– outside the window

  • AD solution: constrained navigation

– underneath other marks

  • AD solution: avoid 3D

– smaller than a pixel

  • AD solution: smart culling
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Guaranteed visibility: Small items

  • naïve culling may not draw all marked items

GV no GV Guaranteed visibility

  • f marks

No guaranteed visibility

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Guaranteed visibility: Small items

  • Naïve culling may not draw all marked items

GV no GV Guaranteed visibility

  • f marks

No guaranteed visibility

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Structural comparison

rayfinned fish lungfish salamander frog mammal turtle bird crocodile lizard snake rayfinned fish bird lungfish salamander frog mammal turtle snake lizard crocodile

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Matching leaf nodes

rayfinned fish lungfish salamander frog mammal turtle bird crocodile lizard snake rayfinned fish bird lungfish salamander frog mammal turtle snake lizard crocodile

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Matching leaf nodes

rayfinned fish lungfish salamander frog mammal turtle bird crocodile lizard snake rayfinned fish bird lungfish salamander frog mammal turtle snake lizard crocodile

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Matching leaf nodes

rayfinned fish lungfish salamander frog mammal turtle bird crocodile lizard snake rayfinned fish bird lungfish salamander frog mammal turtle snake lizard crocodile

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Matching interior nodes

rayfinned fish lungfish salamander frog mammal turtle bird crocodile lizard snake rayfinned fish bird lungfish salamander frog mammal turtle snake lizard crocodile

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Matching interior nodes

rayfinned fish lungfish salamander frog mammal turtle bird crocodile lizard snake rayfinned fish bird lungfish salamander frog mammal turtle snake lizard crocodile

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Matching interior nodes

rayfinned fish lungfish salamander frog mammal turtle bird crocodile lizard snake rayfinned fish mammal lungfish salamander frog bird turtle snake lizard crocodile

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Matching interior nodes

rayfinned fish lungfish salamander frog mammal turtle bird crocodile lizard snake rayfinned fish bird lungfish salamander frog mammal turtle snake lizard crocodile

?

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Similarity score: S(m,n)

T1 T2

A B C D E F A C B D F E

m n

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Best Corresponding Node

T1 T2

A B C D E F A C B D F E

m BCN(m) = n

1/3 2/3 2/6 1/2 1/2

  • computable in O(n log2 n)
  • linked highlighting
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Marking structural differences

T1 T2

A B C D E F A C B D F E

m n

  • matches intuition
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Next Time

  • proposals: by 5pm Mon
  • Thu Nov 5, to read

– VAD Ch. 15: Analysis Case Studies – An Algebraic Process for Visualization Design. Carlos Scheidegger and Gordon

  • Kindlmann. IEEE TVCG (Proc. InfoVis 2014), 20(12):2181-2190.

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