10th
Doctoral Research Workshop
Information
Visualisation
Organised by
Information Visualisation Conference
In cooperation with
&
The Information Visualisation
Conference (iV) is an international
conference that aims to provide a foundation for integrating the human-centred, technological and strategic aspects of information
visualisation in order to promote international
exchange, cooperation and development. Building upon the reported success of
last year workshop, IVS is pleased to announce the 10th Doctoral Research Workshop which will run as
part of the 20th International Conference on Information Visualisation (iV2016).
Doctoral Research workshop
This workshop focuses
on the issues that doctoral students face during their studies and includes following
interactive sessions the theme for this year workshop:
Tutorial
session with Visualization & Data Mining for High Dimensional Datasets
Impact Design
for your research
Tuesday
19 July 2016
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09:00 |
< Universidade NOVA
de Lisboa ● R-Atrium > Registration |
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10:00 - 13:00 |
< Universidade NOVA
de Lisboa ● SBE-217 > Visualization
& Data Mining for High Dimensional Datasets Alfred Inselberg School of Mathematical
Sciences, Tel Aviv University, Tel Aviv, Israel A dataset with M items has 2M subsets anyone of which may be the one fullfiling our objectives. With a good data display and
interactivity our fantastic pattern-recognition can not only cut great swaths
searching through this combinatorial explosion, but also extract insights
from the visual patterns. These are the core reasons for data visualization.
With parallel coordinates (abbr. k-cs) the search
for relations in multivariate datasets is transformed into a 2-D pattern
recognition problem. The foundations are developed interlaced with
applications. Guidelines and strategies for knowledge discovery are
illustrated on several real datasets (financial, process control, credit-score,
intrusion-detection etc) one with hundreds of variables. A geometric
classification algorithm is presented and applied to complex datasets. It has
low computational complexity providing the classification rule explicitly and
visually. The minimal set of variables required to state the rule (features)
is found and ordered by their predictive value. Multivariate relations can be
modeled as hypersurfaces and used for decision
support. A model of a (real) country economy reveals sensitivies,
impact of constraints, trade-offs and economic sectors unknowingly competing
for the same resources. An overview of the methodology provides foundational
understanding; learning the patterns corresponding to various multivariate
relations. These patterns are robust in the presence of errors and that is
good news for the applications. We stand at the threshold of breaching the
gridlock of multidimensional visualization. The parallel coordinates
methodology has been applied to collision avoidance and conflict resolution
algorithms for air traffic control (3 USA patents), computer vision (1 USA
patent), data mining (1 USA patent), optimization, decision support and
elsewhere. KEYWORDS: Exploratory Data Analysis , Classification
for Data Mining , Multidimensional Visualization , Parallel Coordinates ,
Multidimensional/Multivariate Applications Further Details: Visualization & Data Mining for High
Dimensional Datasets |
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13:00 |
< Universidade NOVA
de Lisboa ● R-Atrium > Lunch Break |
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14:00 - 17:00 |
< Universidade NOVA
de Lisboa ● SBE-217 > Doctoral Research Workshop 14.00 Internet of Thing (IoT) in
Healthcare, Pei Ling Lai of Southern Taiwan University of Science and
Technology 14:15 Designing
Research Impact 15:10 Set impact goals for a
specific research project and devise strategies to achieve these 15:30 Break 16:00 Generate number of
action points for generating impact from your research Able to explain how
to measure impact
Make Impact a key
section of your Research 16:45 Group Discussion 17:00 Close |
