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COMP5048 Information Visualisation COMP5048 Course Outline Unit Specification Assumed Knowledge 2011 Semester 2 Tuesday 3-5pm SIT Lecture Theater http://www.it.usyd.edu.au/~shhong/comp5048.htm Lecturer: Seokhee Hong ([email protected]) Consultation: Tuesday 5-6pm Unit Specification Information Visualisation aim to make good pictures of abstract information, such as stock prices, computer networks, social networks and software diagrams. Learning Outcomes Assessment USYD Policies Lecture Time Table References Assumed Knowledge Basic Knowledge in Algorithms/Data Structures Basic Skills in Programming The main challenge is to design and implement effective and efficient algorithms that produce good pictures of abstract data. The unit will review basic concepts, techniques and fundamental algorithms to achieve good visualisation of abstract. Learning Outcomes understanding of basic concepts, techniques and algorithms for good visualisation of abstract data using efficient algorithms to produce good visualisation of abstract data applying effective visualisation methods for specific application area Assessment Week 7: Assignment 1 (10%) Week 11-13: Presentation (10%) Programming Assignment (40%) - group work • Week 9: Report 1 (10%) • Week13: Report 2/demo/presentation (30%) Exam: 40% 1 USYD/SIT Policies Topics Covered: Week 1-7 You are required to carefully read the policies on • Academic Honesty and Plagiarism • Special consideration due to illness and misadventure • No late submissions allowed Approximate schedule: topics are subject to change Week 1 (SH): Introduction Week 2 (SH): Tree Drawing Week 3 (PE): Spring Algorithm Week 4 (PE): Drawing Clustered Graphs Week 5 (SH): Network Analysis Week 6 (SH): Visual Analytics Week 7 (SH): 3D and Interaction References Topics Covered: Week 8-13 Giuseppe Di Battista, Peter Eades, Roberto Tamassia, Ioannis G. Tollis, "Graph Drawing: Algorithms for the Visualization of Graphs", Prentice-Hall, 1999. Approximate schedule: topics are subject to change Michael Kaufmann, Dorothea Wagner eds., “Drawing Graphs - Methods and Models”, Springer-Verlag, Lecture Notes in Computer Science, vol. 2025, 2001. Week 8 (FF): Sugiyama Method Readings in Information Visualization : Using Vision to Think, Stuart Card, Jock Mackinlay, & Ben Shneiderman, Morgan Kaufmann, 1999. Information Visualization : Perception for Design, Colin Ware, Morgan Kaufmann, 2000. Information Visualization by Robert Spence, Pearson Addison Wesley, 2000 Conference Proceedings: GD, IEEE InfoVis, EuroVis, VAST, PacificVis Journals: Week 9 (TH): Evaluation Week 10 (FF): Drawing Planar Graphs Week 11: Student Presentation Week 12: Student Presentation Week 13 : Student Presentation • Information visualization, www.palgrave-journals.com/ivs/ • IEEE Transactions on Visualization and Computer Graphics, http://www.computer.org/tvcg/ Visualisation Lecture 1 Information Visualisation and Graph Drawing Visualisation: the use of computer-supported, interactive, visual representations of data to amplify cognition. • Scientific visualisation: the use of computer-supported, interactive, visual representations of scientific data to amplify cognition. • Information visualisation: the use of computer-supported, interactive, visual representations of abstract data to amplify cognition. 2 Scientific Visualisation Astrophysics - Astronomy Visualisation of the Durham/UKST Galaxy Redshift Survey Andrew Ratcliffe, Physics, University of Durham, U.K Chemistry – Biochemistry Molecular Modelling of Immunosuppressant Molecules Bound to an Enzyme Peter Karuso, School of Chemistry, Macquarie University Chemistry - Biochemistry Molecular Modelling Animation Peter Karuso, School of Chemistry, Macquarie University Geophysics Fly Thru of the Bathymetric Data obtained for East Bass Strait Ben Simons, Sydney VisLab/ Chris Jenkins, Jock Keene, Dept of Geology and GeoPhysics, University of Sydney Information Visualisation the loss of Napoleon’s army Information Visualisation Abstract Data Edward R. Tufte, The Visual Display of Quantitative Information by Charles Joseph Minard (1781-1870) Russian-Polish border 422,000 men / Moscow 100,000 men. Good visualisation Picture Information visualisation research aims to make pictures of abstract data so that humans can understand, navigate, and manipulate the data. Bad visualisation • H. Beck ALP 3 Visualisation of Abstract Information Visualisation of football transfers There are two steps: 1. Analysis: extracting a graph from the information 2. Visualisation: Graph drawing visualisation analysis Data Knowledge Graph Analysis Data Analysis Picture Graph drawing Drew moved from the Panthers to the Eels Miles moved from the Roosters to the Eagles Green moved from the Cowboys to the Roosters O’Hara moved from the Bulldogs to the Raiders .... . . Graph Visualisation of Social Networks email friends Email log files reflect relationships between people Definition: friends network – X and Y are email friends if – X sends more than 5 messages per day to Y, and – Y sends more than 5 messages per day to X. Data Drew moved from the Panthers to the Eels Miles moved from the Roosters to the Eagles Green moved from the Cowboys to the Roosters O’Hara moved from the Bulldogs to the Raiders .... . . Visualisation Graph Drew moved from the Panthers to the Eels Miles moved from the Roosters to the Eagles Green moved from the Cowboys to the Roosters O’Hara moved from the Bulldogs to the Raiders .... . . Picture Visualisation Picture Graph The email_friends graph can be derived from email log files. X Email_friends ( X ) Mary Peter, Albert, DavidF, Alan Judy Bob, Alan Peter Mary, DavidF, Jon DavidF Albert, Joseph, Peter, Mary Jon Peter, Joseph, DavidE DavidE Jon, Joseph, Albert Joseph DavidE, Jon, DavidF Bob Judy, Alan Alan Bob, Mary, Judy Albert DavidF, Mary, DavidE 4 Bad Visualisation Good Visualisation Alan Peter Albert Mary DavidE Joseph Jon Albert DavidF Peter Bob Mary DavidF Judy Alan Jon Bob Judy DavidE Joseph why? why? There are two steps to visualising graphs: 1. Analysis: extracting a graph from the information 2. Graph drawing Reference Model for Visualisation Data Visual Form Main email mates of X Person X Peter, Albert, Judy, DavidF Judy Mary, Bob, Alan Peter Mary, David, Jon David Albert, Joseph, Bob Jon Peter, Joseph, Alan David Peter, Joseph, Albert Joseph David, Jon, David Bob Judy, Alan, David Alan Bob, Jon, Judy Albert David, Mary, David 2. Graph drawing 1. Analysis Email Log Files Mary DavidE Joseph Jon Albert DavidF Peter Mary Raw Data Data Table Data Transformations Visual Structures Visual Mappings Views View Transformations Alan Judy Tree Maps [Johnson, Shneiderman 91] Treemaps: A Space-filling Approach to the Visualization of Hierarchical Information Bob Human Interaction Cone Tree • [Robertson, Mackinlay, Card, CHI 91] "Cone Trees: Animated 3D visualizations of hierarchical information. 5 2D Hyperbolic Tree Browser [Lamping, Rao, Pirolli, CHI’95] A Focus+Context Technique Based on Hyperbolic Geometry for Visualizing Large Hierarchies. Distortion & Hierarchy H3: 3D Hyperbolic M.C. Escher, Circle Limit IV (Heaven and Hell) Walrus - Graph Visualization Tool data: web hyperlinks • quasi-hierarchical graphs: can find reasonable spanning tree using domain-specific information Directory Trees goal: scalability H3: Laying Out Large Directed Graphs in 3D Hyperbolic Space Tamara Munzner, proc. of IEEE Symposium on Information Visualization97 Focus+Context (FishEye View) Network Data Visualisation • [Becker, Eick, Wilks 95] • SeeNet the perspective wall (Mackinlay, Robertson and Card, 1991) [Cox, Eick 95] 3D Displays of Network Traffic SeeNet3D 6 Visualizing the Topology of the MBone Software Visualisation time: 1996 data: MBone tunnels task: find badly placed tunnels goal: simple baseline method: 3D geographic Visualizing the Global Topology of the MBone Tamara Munzner and Eric Hoffman and K. Claffy and Bill Fenner Proceedings of the 1996 IEEE Symposium on Information Visualization, SeeSoft Database Visualisation NicheWorks: Exploring Large Networks NicheWorks - Interactive Visualization of Very Large Graphs, by Graham J Wills. Typical analyses performed using NicheWorks have between 20,000 and 1,000,000 nodes. VISDB Five-dimensional artificially generated data set (100,000 points) in simple configuration. International Calling Fraud Example Overview of calling patterns 40,000 calls involving 35,000 callers International Calling Fraud Example High users' calling patterns 7 International Calling Fraud SPIRE SPIRE-Spatial Paradigm for Information Retrieval and Exploration SPIRE provides a wealth of tools for exploring the information, including query, subset, and trend analysis tools. possible fraud pattern The Israel-Jordan-UAE generated subset zooming in to those callers calling more than one country Galaxies http://www.pnl.gov/infoviz/spire/spire.html Pacific Northwest National Laboratory, USA Galaxies The Galaxies visualization uses the image of stars in the night sky to represent a set of documents. Each document is represented by a single "docustar." Closely related documents cluster together while unrelated documents are separated by large distances. Several analytical tools are provided with Galaxies to allow users to investigate the document groupings, query the document contents, and investigate time-based trends. ThemeView ThemeView The topics or themes within a set of documents are shown as a relief map of natural terrain. The mountains in the ThemeView indicate dominant themes. The height of the peaks indicates the relative strengths of the topics in the document set. Similar themes appear close together, while unrelated themes are separated by larger distances. ThemeView provides a visual overview of the major topics contained in a set of documents. Combined with its exploration tools, ThemeView permits the analyst to identify unanticipated relationships and examine changes in topics over time. 8 The AS Internet graph CAIDA CAIDA, the Cooperative Association for Internet Data Analysis, provides tools and analyses promoting the engineering and maintenance of a robust, scalable global Internet infrastructure. http://www.caida.org The AS Internet graph A Macroscopic Visualisation of the Internet During October, 2000 One of CAIDA's skitter project goals is to develop techniques to illustrate relationships and depict critical components of the Internet infrastructure. The graph reflects 626,773 IP addresses and 1,007,723 IP links of skitter data from 16 monitors probing approximately 400,000 destinations spread across over 48,302 (52%) of globally routable network prefixes. Then aggregate this view of the network into a topology of Autonomous Systems (ASes), each of which approximately maps to an Internet Service Provider (ISP). The abstracted graph consists of 7,624 Autonomous System (AS) nodes and 25,126 peering sessions. Internet Mapping Project Bill Cheswick, Bell Labs and Hal Burch, CMU a long-term project to collect routing data on the Internet. This mapping consists of frequent traceroutestyle path probes, one to each registered Internet entity. They build a tree showing the paths to most of the nets on the Internet. These paths change over time, as routes reconfigure and the Internet grows. They are preserving this data to show how the Internet grows. layout showing the major ISPs. Graph Drawing 9 Graph Drawing Graph Drawing Graphs are abstract structure that are used to model relational information. Graph G=(V,E) •V: set of vertices (objects) •E: set of edges connecting vertices(relationship) Graph Drawing: automatic construction of geometric representations of graphs in 2D or 3D. Hofstadter. Godel, Escher, Bach. [Gansner and North] improved force-directed layouts. Graph Drawing Graph Drawing The classical graph drawing problem is to develop algorithms to draw graphs. The input is a graph with no geometry A - B, C, D B - A, C, D C - A, B, D, E D - A, B, C, E E - C, D The output is a drawing of the graph; the drawing should be easy to understand, easy to remember, beautiful. C C E A B B The input graph is usually some relational description of a software system. The output picture is used in a system design/analysis tool. file edit insert layout agnt agnt(monkey, eat). inst(eat, spoon). obj(eat, walnut). part_of(walnut, shell). matr(spoon, shell). eat _ obj inst spoon X monkey walnut part_of matr shell E A D D The graph drawing problem is to design methods to give good drawings of graphs. Applications Tangled drawing Untangled drawing Software engineering Database Information system Realtime system Computer Network VLSI AI Data Mining Bioinformatics Decision support system Biology Chemistry … 10 Software Engineering - data flow diagram - subroutine call graph - program nesting trees - object oriented class hierarchy Information System - organization charts Data base - entity relationship diagram Graph Drawing Real-time System - Petri nets - state transition diagrams VLSI - circuit schematics Artificial intelligence - knowledge representation diagram Decision Support System - Pert network Graphs tree • free tree • binary tree • rooted tree • ordered tree planar graphs general graphs directed graphs extended graph model • hierarchical graphs • clustered graphs • hyper graphs • higraphs Drawing conventions Drawing conventions polyline drawing straight-line drawing orthogonal drawing grid drawing planar drawing upward drawing convexity … bend Straight-line drawing Polyline drawing Aesthetics agnt eat obj inst part_of walnut matr spoon less readable eat obj Readability is sometimes measured by aesthetic criteria walnut agnt shell monkey Upward drawing Aesthetics readability: the drawing should be easy to read, easy to understand, easy to remember, beautiful. monkey Orthogonal drawing inst spoon part_of matr more readable shell crossings area symmetry edge length • total edge length, maximum edge length, uniform edge length bends • total bends, maximum bends, uniform bends angular resolution aspect ratio 11 Crossings and bends Area and resolution Avoid edge crossings monkey eat obj agnt shell inst part_of walnut matr spoon monkey Avoid edge bends Avoid long edges eat obj agnt shell inst part_of walnut matr spoon One should spread the nodes evenly over the page. This can be measured: • minimise area (for fixed size nodes) or equivalently • maximise resolution (for a fixed size screen). less readable Aesthetics NP-hardness There are many aesthetic criteria for good diagrams: • minimum edge crossings, • minimum bends, • minimum edge lengths, • maximum resolution, and many more. agnt monkey eat obj inst spoon walnut part_of matr minimize crossings minimize area maximize symmetry minimize total edge length minimize number of bends maximize angular resolution … Conflicts Minimize edge crossings Maximize symmetry shell more readable Graph Drawing Algorithms Tree Drawing •Tidy drawing •Free tree drawing Planar Graphs •Straight-line drawing •Orthogonal (grid) Drawing Undirected graphs: Spring algorithm (force directed methods) Directed graphs : Sugiyama method (Layered/Hierarchical drawing) Clustered graphs … Latour tree visualization system I. Herman, G. Melançon, M.S. Marshall,VisSym99 Radial layout of 29773 nodes Reingold-Tilford layout of 3255 nodes 12 On-line Graph Navigation System Graphviz, AT&T, USA Maolin Huang73 OGDF, Germany GDToolkit, Italy Hermes: internet topology titletitle Tom Sawyer Software, USA Social network Hierarchical layout Symmetric layout Circular layout 13 Actor Collaboration Network A Few Good Man Days of Thunder (1990) Far and Away (1992) Eyes Wide Shut (1999) Kevin Bacon Number 1 Kevin Bacon Number 2 IEEE InfoVis Research Important researchers (research groups) with their research area Ahmed et al.: EuroVis 05 (IEEE InfoVis 2004 competition winner) Evolution of research area Visualisation of FIFA 2002 World Cup Visualisation of Stock Market Data Visualisation of Fund Manager Movement Graph Tim Dwyer Ahmed, Fu, Hong, Quan, Xu: GD 2006 competition winner 14 Visualisation of Patterns – motif “Motifs”: small pattern in the network which occurs with significantly high frequency. Data: transcriptional regulation network of Escherichia coli. Visualisation and Analysis of Network Motifs (IV 2005) W. Huang, C. Murray, X. Shen, L. Song, Y. Wu, L. Zheng. Visualization of Protein Interaction Network Visualization of Biochemical Pathways Falk Schreiber Map of protein-protein interactions. The colour of a node signifies the phenotypic effect of removing the corresponding protein (red, lethal; green, non-lethal; orange, slow growth; yellow, unknown). By Hawoong Jeong Homework Examples of Good Visualisation Bad Visualisation send me a ppt slide with pictures + source: data, paper, author, website by next monday 15