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14th International Conference on Information
Visualisation – IV10 |
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Francis T. Marchese, 2. Usability Evaluation and
Information Visualisation Keith
Andrews, Graz University of Technology, Austria 3. Fundamentals
of visual data mining, information retrieval, extraction, and analysis Haim Levkowitz, Department
of Computer Science University of Massachusetts Lowell, USA 4. Object and Spatial Database Ray Kresman, Bowling Green State University,
Bowling Green, OH, USA  |
A Full-day Course: Monday
26th July 2010, Time: 10:30 -16:30
Art for Visualizers
Francis T.
Marchese, Pace University, NY, NY 10038, USA
http://csis.pace.edu/~marchese
Abstract
Lectures, panels, and
symposia that explore issues at the intersection of art and visualization have
become a recurring theme at visualization conferences. Since 2003 the U.S.
National Science Foundation and the journal Science have sponsored an
International Science and Engineering Visualization Challenge in which many of
the winning entries exhibit noteworthy aesthetic qualities that may be
considered artistic in nature. And inspirations from artistic movements and
practice have stimulated visualization research, particularly in the
application of non-photorealistic or expressive rendering techniques to
visualization problems.
The confluence of art
and visualization has a long history. Indeed, the Paleolithic artists who
painted on the cave walls of southwest France may have been the first
visualizers. Or was it vice versa? Either way, throughout the intervening
millennia visual artists have become proficient at transforming information into
representations that are designed to communicate and provoke. The challenge
facing a viewer of art is how to decipher an image’s content and extract its
meaning. This holds true for a viewer of visualizations as well.
Thus, the purpose of
this tutorial is to introduce the fundamental skills for analyzing visual art
that subsequently may be applied to scientific and information visualizations.
It will offer an historical survey of the intersections of art and
visualization with an emphasis on examples from contemporary artists, and
provide an opportunity for participants to practice these skills within a
gallery setting. To this end, the tutorial will be composed of two sessions. A
morning session will focus on an historical survey, conceptual foundations, and
skill acquisition. An afternoon session convening at The National Gallery of
Art (Trafalgar Square), will allow course participants to test their analysis
skills on a selection of the gallery’s paintings.
Organisation
Level of Tutorial: Introductory
Biography
Frank Marchese is Professor of
Computer Science at Pace University where he teaches courses in computer
graphics, visualization, human-computer interaction, and software engineering.
His research interests span scientific and information visualization; novel
user interfaces for visualization; distributed and collaborative visualization;
integration of visualization into lifecycles for scientific research and
software engineering; and the development of visualization systems at the
intersection of art, science, and technology.
He is founder and
Director of Pace’s Center for Advanced Media (CAM) and the Pace Digital
Gallery, the latter of which is collaboration between Pace University’s
Seidenberg School of Computing and Department of Fine Arts. He has published
widely in science, technology, and art; is editor of the conference proceedings
entitled Understanding Images published by Springer-Verlag, and is co-chair of
Information Visualization 2010 (IV’10).
Dr. Marchese has a
Ph.D. in quantum chemistry from the University of Cincinnati and was a National
Institutes of Health Postdoctoral Research Fellow specializing in the
statistical mechanics of liquids. He has been twice awarded Pace’s School of
Computing Excellence in Research Award, received the Kenan Award for Teaching
Excellence, and been nominated for The Carnegie Foundation Teacher of the Year
Award. In December 2008, he was awarded Pace University’s Faculty Award for
Distinguished Service. He is currently a visiting scholar at New York
University’s Institute of Fine Arts where he is studying museum curation, the
relationship between text and image in medieval art, and the artistic origins
of information visualization.
A Full-Day Course: Mon 26
Jul 2010, 10:30-17:30
Usability Evaluation and Information Visualisation
Prof. Keith
Andrews, Graz University of Technology, Austria
http://www.iicm.edu/keith
Abstract
The first part of this
tutorial will look at usability evaluation in general. It will cover both
usability inspection methods (such as heuristic evaluation, cognitive
walkthrough, and action analysis) and usability testing methods (such as
thinking aloud tests, formal experiments, and usage studies).
The second part of the
tutorial will look specifically at the evaluation of information visualisations. We will step through the design, analysis,
and reporting of two usability studies of infovis systems:
a formative evaluation (thinking aloud test) and a comparative study (formal
experiment).
Organisation
Level
of Tutorial:
Introductory level
Keith Andrews is a tenured associate professor at
the Institute for Information Systems and Computer Media (IICM) at Graz
University of Technology, in Austria. His research interests include
information visualisation,
human-computer interaction, and the internet. He
has a B.Sc.(Hons) in Mathematics
and Computer Science from the University of York, England, and an M.Sc. and
Ph.D. in Technical Mathematics/Computer Science from Graz University of
Technology.
Having lead the Harmony (Unix/X11 browser for Hyperwave)
and VRwave (VRML browser) projects for several years,
he is currently pursuing research in the field of information visualisation.
Keith was program co-chair of the IEEE Symposium on Information Visualization
in 2001 and 2002, and general chair in 2005.
Keith teaches
undergraduate level courses on Internet and New Media, Human-Computer
Interaction, and User Interface Design, and graduate level courses on
Information Visualisation and Information Architecture
and Web Usability, as well as various short courses and tutorials at conferences
and for companies.
A Full-Day Course: Mon 26
Jul 2010, 10:30-17:30
Fundamentals of visual data
mining, information retrieval, extraction, and analysis
Haim
Levkowitz, Associate Professor and Co-Director, Institute
for Visualization and Perception Research And Graphics
Research Laboratory
Department of
Computer Science University of Massachusetts Lowell, USA
Abstract
Everyone knows how to
"Google"; some people even know that Google is a "search
engine"; but very few know that "search engines" are
"information retrieval" engines. As the amount of information grows
so rapidly, finding the right information, and analyzing it has become more and
more challenging. Search technology has -- probably -- been the fastest- and
steepest-growth segment, ever. And when you find information, that's just about
the beginning of the next challenge: extracting meaning and knowledge out of
it.
Today, most of your search
queries are formulated by (usually very few) key words -- a very difficult way
to express the semantic of your search needs. And the results appear as (very
long) lists of text. To find what you've been looking for -- or to find out
that it is not there -- you need to scan through page after page after page of
results, not a very efficient or effective process. Further, if you are trying to
find non-textual information (images, sounds), you have very limited resources.
Can we do better than that?
Yes. How? By replacing the sequential search through results' text with
perceptually-stronger visual mechanisms, often referred to as visual text (or
data) mining.
The goals of this course will
be:
1. to explore the fundamentals of Information Retrieval and
Mining;
2. to understand the basics of visual text and data mining;
3. to learn the most powerful information and knowledge
extraction techniques;
4. to understand in what way non-text search and retrieval is
different, and in what ways it is similar to text retrieval; and
5. to understand how the combination of these methods can make
search much more powerful and effective.
Students will learn basic and advanced
information retrieval techniques, visual text and data mining approaches,
information and knowledge extraction methods, and their combined
applications. In addition to text-based
search, the course will examine retrieval of non-textual information (such as
images, sounds, video, or any other non-text information) based on non-textual
features, not just on text metadata.
The second part of the course will focus on
mining and analysis, with a goal to extract meaning and knowledge out of the
retrieved information.
Who should attend: researcher and
practitioners in the fields of information retrieval and extraction, search,
data mining, and visual analytics, as well as students aspirating to enter
these fields.
Haim Levkowitz
is an associate professor of computer
science and co-director of the Institute for Visualization and Perception
Research at the University of Massachusetts Lowell, in Lowell, MA, USA. He is a
world-renowned authority on visualization, perception, color,
and their application in data mining and information retrieval. He is the
author of "Color Theory and Modeling
for Computer Graphics, Visualization, and Multimedia Applications"
(Springer 1997) and co-editor of "Perceptual Issues in Visualization"
(Springer 1995), as well as many papers in these subjects. He has more than 35
years experience in teaching and lecturing, and has taught many tutorials and
short courses, in addition to regular academic courses.
A Half-Day Course: Mon 26
Jul 2010, 14:00-17:0
Ray
Kresman, Professor of Computer Science at Bowling Green State
University, Bowling Green, OH, USA
Abstract
With the advent of mobile and
wireless-enabled devices, a number of graphical and location enabled applications,
which have moved well beyond the realm of traditional geographical information
systems, are emerging. For example, distance between two points in a city
involves more than the location coordinates, the underlying themes such as
street networks are required. An understanding of spatial and object database
concepts is critical to the design of applications that capture the spatial
dimension of information. However, these relatively new ideas are less well
understood by students; few computer science programs cover them in their
database, computational geometry, and graphics course offerings. This tutorial
introduces the participants (educators, students, software developers) to the
use of object-oriented and spatial database primitives in application design. We
describe how to store and manipulate graphical objects such as points, line
segments, and more complex objects such as curves and polygons and maps. We
show how features can be accessed interactively and through programming
languages (Java). Where applicable, we illustrate these concepts using public
domain database software (MySQL) and commercial software
(Oracle). Finally, we explain how web-enabled and other applications can take advantage
of mapping features -- for example, Google Maps API -- to enhance their visual appeal.
Objectives:
• Compare
and contrast GIS and spatial databases.
• Draw
parallel between object oriented programming and object databases.
• Create
and use database objects with data members and member functions.
• Understand
use of Geometry data type.
• Construct
and respond to spatial queries.
• Learn
how to query metadata
• Understand
XML interface to maps and how to render maps.
• Access
database objects and geometry from programs.
Intended audience: text and multimedia data miners;
conference attendees, especially educators, upper level computer science and or
geography students who may or may not have had a course on spatial database,
and software developers.
Background of the
Audience: Exposure
to relational DBMS. Basic proficiency in objectoriented
concepts in Java and/or C++.
Level of Tutorial: Introductory level
Ray Kresman is a Professor of Computer Science at
Bowling Green State University, Bowling Green, OH. His applied computer science
interests include computer security and web-to-database connectivity,
three-tier architectures and secure internet technologies, and data
warehousing. Dr. Kresman's work on distributed
systems was supported by the National Science Foundation. He has published
widely in the area of distributed systems and complexity of algorithms.
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