Information Visualisation Praminda Caleb-Solly. Learning Objectives Gain an understanding of the benefits of information visualisation Explore ways of.

Slides:



Advertisements
Similar presentations
Office of SA to CNS GeoIntelligence Introduction Data Mining vs Image Mining Image Mining - Issues and Challenges CBIR Image Mining Process Ontology.
Advertisements

Prof. Carolina Ruiz Computer Science Department Bioinformatics and Computational Biology Program WPI WELCOME TO BCB4003/CS4803 BCB503/CS583 BIOLOGICAL.
Information Retrieval: Human-Computer Interfaces and Information Access Process.
Visualization CSC 485A, CSC 586A, SENG 480A Instructor: Melanie Tory.
Visualizating the Non-Visual: Spatial Analysis and Interaction with Information from Text Documents J.A. Wise, J.J. Thomas, K. Pennock, D. Lantrip, M.
MS DB Proposal Scott Canaan B. Thomas Golisano College of Computing & Information Sciences.
CPSC 695 Future of GIS Marina L. Gavrilova. The future of GIS.
© Prentice Hall1 DATA MINING TECHNIQUES Introductory and Advanced Topics Eamonn Keogh (some slides adapted from) Margaret Dunham Dr. M.H.Dunham, Data Mining,
Multiscale Visualization Using Data Cubes Chris Stolte, Diane Tang, Pat Hanrahan Stanford University Information Visualization October 2002 Boston, MA.
Information Visualization Mike Brzozowski cs376 October 28, 2004.
Information Retrieval: Human-Computer Interfaces and Information Access Process.
Information Visualization. Information Visualization (Ch. 1), Stuart K. Card, Jock D. Mackinlay, Ben Shneiderman in Readings in Information Visualization:
ICS 280: Information Visualization 1 ICS 280: Advanced Topics in Information Visualization Professor Alfred Kobsa Overview of Information Visualization.
Chapter 12: Simulation and Modeling Invitation to Computer Science, Java Version, Third Edition.
WPI Center for Research in Exploratory Data and Information Analysis From Data to Knowledge: Exploring Industrial, Scientific, and Commercial Databases.
Semantic Web Technologies Lecture # 2 Faculty of Computer Science, IBA.
Architectural Design.
Chapter 5 Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization.
Data Mining Techniques
Document (Text) Visualization Mao Lin Huang. Paper Outline Introduction Visualizing text Visualization transformations: from text to pictures Examples.
Ihr Logo Chapter 5 Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization Turban, Aronson, and Liang.
Semantic Web Technologies ufiekg-20-2 | data, schemas & applications | lecture 21 original presentation by: Dr Rob Stephens
Information Visualization Learning Modules Katy Börner, InfoVis Lab, School of Library and Information Science Indiana University, Bloomington Motivation.
Visual User Interfaces David Rashty. “Grasping the whole is a gigantic theme. Arguably, intellectual history’s most important. Ant-vision is humanity’s.
Learning Object Metadata Mining Masoud Makrehchi Supervisor: Prof. Mohamed Kamel.
Publishing and Visualizing Large-Scale Semantically-enabled Earth Science Resources on the Web Benno Lee 1 Sumit Purohit 2
1 Adapting the TileBar Interface for Visualizing Resource Usage Session 602 Adapting the TileBar Interface for Visualizing Resource Usage Session 602 Larry.
Dept. of Computing Science, University of Aberdeen1 CS4031/CS5012 Data Mining and Visualization Yaji Sripada.
Introduction to Data Mining Group Members: Karim C. El-Khazen Pascal Suria Lin Gui Philsou Lee Xiaoting Niu.
Introduction to Information Visualization Robert Putnam Introduction to Information Visualization - Spring 2013.
Modeling Applied Mindtool Experiences with Hyperlinked Presentation Software Stephenie Schroth Jonassen, D.H. (2006). Modeling with Technology: Mindtools.
© 2010 Pearson Addison-Wesley. All rights reserved. Addison Wesley is an imprint of Designing the User Interface: Strategies for Effective Human-Computer.
Metadata and Geographical Information Systems Adrian Moss KINDS project, Manchester Metropolitan University, UK
Fall 2002CS/PSY Information Visualization Picture worth 1000 words... Agenda Information Visualization overview  Definition  Principles  Examples.
Data Mining Chapter 1 Introduction -- Basic Data Mining Tasks -- Related Concepts -- Data Mining Techniques.
Presented by Nassib Awad
Network Ontology Ramesh Subbaraman Soumya Sen UPENN, TCOM 799.
Ihr Logo Chapter 5 Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization Turban, Aronson, and Liang.
Research Design for Collaborative Computational Approaches and Scientific Workflows Deana Pennington January 8, 2007.
Integrating GVis, GIS and KDD for Exploring Spatio-Temporal Data Integrating GVis, GIS and KDD for Exploring Spatio-Temporal Data Monica Wachowicz Wageningen.
Data Mining – Intro. Course Overview Spatial Databases Temporal and Spatio-Temporal Databases Multimedia Databases Data Mining.
A Context Model based on Ontological Languages: a Proposal for Information Visualization School of Informatics Castilla-La Mancha University Ramón Hervás.
Chapter 5: Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization DECISION SUPPORT SYSTEMS AND BUSINESS.
User Profiling using Semantic Web Group members: Ashwin Somaiah Asha Stephen Charlie Sudharshan Reddy.
© 2010 Pearson Addison-Wesley. All rights reserved. Addison Wesley is an imprint of Designing the User Interface: Strategies for Effective Human-Computer.
Distributed Data Analysis & Dissemination System (D-DADS ) Special Interest Group on Data Integration June 2000.
Advanced Visualization Overview. Course Structure Syllabus Reading / Discussions Tests Minor Projects Major Projects For.
Lucent Technologies - Proprietary 1 Interactive Pattern Discovery with Mirage Mirage uses exploratory visualization, intuitive graphical operations to.
A Portrait of the Semantic Web in Action Jeff Heflin and James Hendler IEEE Intelligent Systems December 6, 2010 Hyewon Lim.
Michael Hamilton, Vanessa Rivera del Rio and Sean Askay University of California James San Jacinto Mountains Reserve.
Books Visualizing Data by Ben Fry Data Structures and Problem Solving Using C++, 2 nd edition by Mark Allen Weiss MATLAB for Engineers, 3 rd edition by.
Information Visualization Theresa Nguyen 4/10/2001.
1 Visual Encoding Andrew Chan CPSC 533C January 20, 2003.
Framework and Models. Framework To help understand the field To develop a system that will allow us to ▫ Develop good designs ▫ Test ▫ Evaluate We need.
Pattern Recognition. What is Pattern Recognition? Pattern recognition is a sub-topic of machine learning. PR is the science that concerns the description.
Ontologies COMP6028 Semantic Web Technologies Dr Nicholas Gibbins
1 Survey of Profiles from Other Domains XMSF Profile SG 13 January 2004 Curt Blais and NPS MV3250 (Introduction to XML, 1st Quarter 2005) Katherine L.
Semantic Web Technologies Readings discussion Research presentations Projects & Papers discussions.
Computer Representation of Venn and Euler Diagrams Diunuge B. Wijesinghe, Surangika Ranathunga, Gihan Dias Department of Computer Science and Engineering,
Chapter 12: Simulation and Modeling
XML Related Technologies
Introduction Multimedia initial focus
Introduction to Data Mining
Invitation to Computer Science 5th Edition
Information Visualization Picture worth 1000 words...
Data Warehousing and Data Mining
CSc4730/6730 Scientific Visualization
حيـــم الر حمن الر الله بســـم.
Data Mining: Concepts and Techniques
CHAPTER 14: Information Visualization
Presentation transcript:

Information Visualisation Praminda Caleb-Solly

Learning Objectives Gain an understanding of the benefits of information visualisation Explore ways of visualising different types of information

What is Visualisation? Definitions of Visualisation: –graphical presentation of information, often dependent on categorisation or clustering techniques to bring out patterns in the information. members.optusnet.com.au/~webindexing/Webbook2Ed/glos sary.htm members.optusnet.com.au/~webindexing/Webbook2Ed/glos sary.htm –Display of data in a manner meaningful to the user. This doesn't necessarily imply sophisticated multi-dimensional graphics. In many cases tradition 2D line graphs are the most meaningful method of interpretation. Definitions of scientific visualisation: –Scientific visualization is a branch of computer graphics which is concerned with the presentation of interactive or animated digital images to scientists who interpret potentially huge quantities of laboratory or simulation data or the results from sensors out in the field. en.wikipedia.org/wiki/Scientific_visualisation en.wikipedia.org/wiki/Scientific_visualisation

Information Visualization Applicability Bergeron has defined the following classification –exploratory visualization (undirected search) –analytical visualization (directed search) –descriptive visualization Decision making

What are the benefits of Information Visualisation? Parallel Perceptual Processing Offload Work from Cognitive to Perceptual System Expanded Working Memory Expanded Storage of Information Locality of Processing High Data Intensity Spatially Indexed Addressing Recognition Instead of Recall Abstraction and Aggregation Visual Representations Make Some Problems Obvious Perceptual Monitoring Manipulability of Medium

Some Visualisation Demos Treemapshttp:// /amazon/ htmlhttp:// /amazon/ html LifeLines Scientific Visualisation\1998_lifelines.mpgScientific Visualisation\1998_lifelines.mpg Visual Thesaurus

Process for visualisation Source:

A process for visualization? Card, Stuart K., Mackinlay, Jock D. & Shniederman, Ben. (1999). Readings in Information Visualization: Using Vision to Think. Academic Press.

Visualization and Ontologies What is an ontology: –An explicit formal specification of how to represent the objects, concepts and other entities that are assumed to exist in some area of interest and the relationships that hold among them –The hierarchical structuring of knowledge about things by subcategorizing them according to their essential (or at least relevant and/or cognitive) qualities

T. Berners-Lee et al, in Scientific American, May 2001

SVG Demos SVG is an acronym for Scalable Vector Graphics and is a W3C standard. It's a language for describing two- dimensional graphics and graphical applications in XML.

Further Demos leDemo/RectangleDemo.viewlet/Rectangle Demo_launcher.htmlhttp:// leDemo/RectangleDemo.viewlet/Rectangle Demo_launcher.html Further example veSVGExamples.htm veSVGExamples.htm

References Bergeron, D. (1993) Visualization reference models (panel session position statement). In G.M. Nielson and D. Bergeron, editors, Proceedings of Visualization '93, IEEE Computer Science Press. Card S., Mackinlay J, and Shneiderman B. (1999) Readings in Information Visualization: Using Vision to Think, Morgan Kaufmann. Spence R. (2001) Information Visualization, ACM Press/Addison Wesley Tufte, E. 2001, The Visual Display of Quantitative Information, Graphics Press Ware C. (2000) Information Visualization: Perception for Design, Morgan Kaufmann

Enabling Technologies XSLT style sheets that generate SVG output based on XML input –XSLT engine to transform the source data and an SVG viewer RDF for ontology definition

Issues in Scientific Data Management Creating Collections Physical Data Handling Interoperability between collections Data Ownership and Security Persistence Metadata definition Knowledge Discovery Data dissemination and presentation

Some Application Areas Chemistry Genomics Astronomy Geography Bioinformatics

1932 London Underground Map

Harry Beck’s 1933 London Tube Map

A recent tube map

What are the benefits of Information Visualisation? Card, Stuart K., Mackinlay, Jock D. & Shniederman, Ben. (1999). Readings in Information Visualization: Using Vision to Think. Academic Press.