Visual Analysis of MultiAttribute Rankings Samuel Gratzl Alexander Lex Nils Gehlenborg Hanspeter Pfister and Marc Streit Background Rankings S tructuring unorganized collections of items ID: 547968
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Slide1
LineUp: Visual Analysis of Multi-Attribute Rankings
Samuel
Gratzl
, Alexander
Lex
, Nils
Gehlenborg
,
Hanspeter
Pfister
, and Marc
StreitSlide2
Background
Rankings
S
tructuring unorganized collections of items.
C
omputing a rank for each item based on the value of one or more of its attributes.
Usage of Ranking
To arrange tasks or to evaluate the performance of products relative to each other.
University ranking
Restaurant ranking
Laptop rankingSlide3
Background
Problem
The visualization of a ranking itself is straightforward, but its interpretation is not.
How there attributes contribute to the rank.
How changes in one or more attributes influence the ranking.
To interpret, modify, and compare such rankings.
A
dvanced visual tools are needed to make this process efficient.Slide4
Contributions
A new technique that addresses the limitations of existing methods.
A comprehensive analysis of requirements of multi-attribute rankings considering various domains.
The design and implementation of
LineUp
, a visual analysis tool for creating, refining, and exploring ranking based on complex combinations of attributes.Slide5
Requirement analysis
Encode rank (1/10)
Users of the visualization should be able to quickly grasp the ranks of the individual items.
Encode cause of rank (2/10)
In order to understand how the ranks are determined, users must be able to evaluate the overall item scores from which the ranking is derived and how they relate to each other.
Support multiple attributes (3/10)
Users must be able to combine multiple attributes to produce a single all-encompassing ranking.Slide6
Requirement analysis
Support filtering (4/10)
Users might want to exclude items from a ranking for various reasons.
Enable flexible mapping of values to scores (5/10)
The ranking visualization must allow users to flexibly normalize attributes.
Adapt scalability to the task (6/10)
There is a tradeoff between level of detail and scalability.Slide7
Requirement analysis
Handle missing values (7/10)
As real-world data is often incomplete, it must be able to deal with missing values.
Interactive refinement and visual feedback (8/10)
To enable users to judge the effect of modifications which can influences on rankings. Such as dynamically add and remove attributes, modify attribute combinations, and change the weights and mappings of attributes.Slide8
Requirement analysis
Rank-driven attribute optimization (9/10)
Optimizing the settings (values, weights) to find the best possible ranking of a particular item.
Compare multiple rankings (10/10)
An interactive ranking visualization that fulfills 1-9 is a powerful tool addressing many different tasks. However, in some situations users are interested in putting multiple rankings into context with each other.Slide9
Multi-Attribute Ranking Visualization Technique
LineUp
A
n interactive technique designed to create, visualize, and explore rankings of items based on a set of heterogeneous attributes.
The visualization uses bar chars in various configurations for ranking.Slide10
Illustration of different ranking visualization techniquesSlide11
LineUp
Vedio
http://www.youtube.com/watch?v=iFqCBI4T8ksSlide12
Evaluation
User Study
Eight
participants (6 male,
2 female
) between 26 and 34 years old. They are all researchers
or students
with a background in computer science, bioinformatics,
or public
health
.
Compare with Excel and Tableau.
Most of them were convinced that
LineUp
would save time and allow them to gather more insights.Slide13
Questions ?Thanks