Presentation is loading. Please wait.

Presentation is loading. Please wait.

Graph Representation D.J. Duke Department of Computer Science University of Bath, U.K.

Similar presentations

Presentation on theme: "Graph Representation D.J. Duke Department of Computer Science University of Bath, U.K."— Presentation transcript:

1 Graph Representation D.J. Duke Department of Computer Science University of Bath, U.K.

2 Overview The challenge of scale Knowledge and cognition –kinds of knowledge –human information processing Art and Rendering –drawing and denotation systems –NPR Enhancing visualization Some of the open issues …

3 Visualization Motivations: –presentation: “See this!” –confirmation: “Can we see this?” –exploration: “What can we see?” Shift work from logical to perceptual Utilise latent knowledge

4 Example /dVTK /dtalks /dtools VTKLocal VTKRendering VTKvtkbin vtkbinbin vtkbinDebug vtkbinWrap LocalvtkAdjList.h LocalvtkAdjList.cxx binvtk.exe DebugvtkAdjList.obj DebugvtkAdjListTcl.obj WrapvtkAdjListTcl.cxx /d talks tools VTK Local vtkbin Rendering vtkAdjList.h vtkAdjList.cxx Debug bin Wrap vtkAdjListTcl.cxx vtkAdjListTcl.obj vtkAdjList.obj Vtk.exe

5 The challenge of scale Technology pushing level of ambition: –size of datasets –complexity of relationships within the data –complexity of the underlying domain Some of the solutions: –streaming, parallelism, cluster hardware –level of detail control Is the user becoming the bottleneck?

6 Approaches to scale Scientific visualization – McCormick et al. Information visualization – Card et al.

7 Research issues Large-scale datasets Need for abstractions –what is a useful overview? –support and encourage interaction Need for composition –info. / sci. vis. distinction is not useful –implications for user and system

8 Human inference Levels of inference (Gahegan / MacEachren) –abduction: classification from experience –induction: classification from attributes –deduction: classification from rules Sources of meaning –public –private How and where does a representation work?

9 Cognitive interpretation Interacting Cognitive Subsystems –Barnard, 1979 - –generic processing unit –cognition distributed across 9 systems –principles of information processing Use of ICS (Barnard, May, Scott et al.) –clinical psychology; emotion –CTA (Cognitive Task Analysis) –display decomposition

10 Levels of interpretation OBJVIS LIM IMPLIC PROP ACMPL ART node p540 +-[parent]- p539 +-[children] – [many] node p741? parent = “down” … BS

11 Subsystem operation Incoming representations Blending at input array Transformed into output representations Copied into episodic memory... … and revived

12 Memory and learning Novel representations require intervention of “central engine” PIP-loop Over time subsystem image record forms generalised records, e.g. CTRs Eventually, knowledge becomes proceduralized implic prop

13 Implicational channels Much visualization uses structural channel Implicational “emotive” interpretation is also available … TaketeUloomo


15 Non-Photorealistic Rendering (NPR) Schumann, Strothotte, Raab & Laser: Assessing the affect of non-photorealistic rendered images in CAD. In Proc. CHI’96, ACM Press

16 The “art” of visualization Representation: –geometric structure –attribute mappings Design issues: –perceptual cues –knowledge assumptions –kinds of inference General problem in visualization “‘Good’ visualizations mix metaphors” [Hanrahan]

17 C.elegans cell lineage, Sulston From “To Draw a Tree”, P. Hanrahan, 2001

18 Machine-part assembly From “To Draw a Tree”, P. Hanrahan, 2001

19 Drawing on art What can we learn from artistic techniques? Interest in European school of realism vs Japanese/Chinese traditions

20 Ship of Fools [Detail] Bosch

21 View of Delft Vermeer

22 Boating on the river below a Buddhist temple Wu Li

23 Artistic Traditions Japanese / Chinese painting –relationship to philosophy? European school of realism –studies of perspective (Dürer) –studies of physiology (da Vinci) Photography 20 th century: impressionists, cubists …

24 Les Tuileries Pissarro

25 Le Jardin Monet

26 Katata Hiroshige

27 Drawing Systems From “The Draughtsman’s Contract” by J. Willats Spatial relationship between objects Different kinds of fidelity: –faithful to appearance –faithful to shape Visualization –how is space used? –what concept(s) does space capture?


29 Royere tool (

30 Denotation system Relation between marks and real world. Painting: –Marks represent pattern of light intensities –Visual system interprets as shapes Drawing –What do lines stand for? Visualization –What does an edge in a graph represent?

31 Why denotation matters time Residual concentration

32 Example (1/2) Multiple signs: –Nodes –Edges –Elided regions Latour tool (

33 Example (2/2) combine multiple –sources (context + data) –techniques (stream-surface + scalar) –levels of detail –levels of certainty VTK LOxSurface2 example (

34 Art and visualization: summary For the eyes: –distinguish different kinds of mark –new channel to encode properties of data For the emotions: –encourage interaction and exploration For the intellect: –convey different types of data

35 Systems issues Scientific visualization: –generic representations: streamlines, iso-surfaces, … –generic algorithms: marching cubes, … –modular toolkits: AVS, Iris Explorer, VTK, … Information visualization: –generic representations: trees, cushion maps, … –generic algorithms: Reingold-Tilford, … –tools?

36 Modularity Simple dataset model helps sci.vis.: –generic algorithms –composable representations Aim to achieve similar in info.vis.: –combine distinct representations –support novel info.vis. algorithms –build on infrastructure, e.g. streaming

37 A Unified View Data = geometry + topology + attributes –Geometry = points in the space –Topology = organization of points into cells Implementation: implicit or explicit

38 Abstract points and cells Points = data items Cells = relationships between data a b c d e Points = a … e j k p m l n o Cells = j... p

39 Cells as records BOZ (Casner, 1991) –Query database of flight information –Design display of results relative to task AMS-ZUR £60 080009001000110012001300 BRS-AMS £110 AMS-ZUR £140 LHR-ZUR £176 LHR-AMS £105 LHR-ZUR £176

40 BOZ (cont.) from = LHR to = ZUR dept = 0800 arr = 0930 price = £176 avail = yes from = LHR to = AMS dept = 0945 arr = 1055 price = £105 avail = no from = LHR to = ZUR dept = 1130 arr = 1300 price = £176 avail = yes from = AMS to = ZUR dept = 0900 arr = 1030 price = £60 avail = yes from = AMS to = ZUR dept = 1100 arr = 1230 price = £140 avail = yes from = BRS to = AMS dept = 0830 arr = 0955 price = £110 avail = yes

41 VTK Visualization Toolkit –C++ class library (~ 600 classes) –Wrappers for Tcl, Python, Java –Open source: Separation of data and process objects –Datasets – separate slide –Process objects: source, filter, mapper

42 ? VTK in action Process execution demand-driven Only execute if output needs updating datafilevtkPolyDataReadervtkPolyDataNormalsvtkMapper vtkGlyph3D vtkSphereSource vtkMapper

43 VTK Data Sets vtkObject vtkDataSet vtkRectilinearGridvtkPointSetvtkImageData vtkPolyDatavtkStructuredGridvtkUnstructuredGrid vtkDataObject vtkTopology vtkGeometryvtkGraph vtkPolyData vtkGraph vtkDataObject

44 Tree pipeline Source file vtkProgrammableSource vtkConeTree vtkStrahlerMetric vtkGraphGeometryFilter vtkFieldDataToAttributeDataFilter vtkTubeFilter vtkPolyDataMapper vtkLookupTable vtkActor vtkPolyDataMapper vtkActor vtkGlyph3D vtkCubeSourcevtkTransform vtkTransformPolyDataFilter vtkRenderer

45 From trees to graphs Module for general graph layout: –spanning DAG –tree layout –graph Management of edge bends: –add pseudo-nodes, ignore for node drawing –apply layout positions to “real nodes” –better: use polyline mechanism? GenGraphLayout3D graph DAG layout

46 Initial results Web site cone-tree layout Strahler metric splatting FSM simulation 3D graph layout generalized Strahler

47 Further work Extensions to basic tools –interaction techniques, e.g. brushing –multiple representations –minimal rendering of overview –attribute management Significant support already present –level-of-detail control –pluggable interactors –multiple viewports

48 Summary The brain is not subject to Moore’s law. Visualization is not a panacea … –users’ mental representations –combine discrete / “continuous” models Knowledge representation –public / private levels [MacEachren] –integration with other representations Flexible tools are a first step.

49 Thanks … David AuberLABRI, Uni. Bordeaux, France Phil BarnardMRC Cognition & Brain Science Unit, UK David DuceComp. Sci, Oxford Brookes University, UK Ivan HermanW3C, Amsterdam, The Netherlands Scott MarshallGlaucus Proteomics B.V., The Netherlands Jon MayPsychology, University of Sheffield, UK UK Engineering and Physical Sciences Research Council

Download ppt "Graph Representation D.J. Duke Department of Computer Science University of Bath, U.K."

Similar presentations

Ads by Google