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LineUp : Visual Analysis of Multi-Attribute Rankings. Samuel Gratzl , Alexander Lex , Nils Gehlenborg , Hanspeter Pfister , and Marc Streit. Background. Rankings S tructuring unorganized collections of items.
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LineUp: Visual Analysis of Multi-Attribute Rankings Samuel Gratzl, Alexander Lex, Nils Gehlenborg, HanspeterPfister, and Marc Streit
Background • Rankings • Structuring unorganized collections of items. • Computing 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 ranking
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. • Advanced visual tools are needed to make this process efficient.
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.
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.
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.
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.
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.
Multi-Attribute Ranking Visualization Technique • LineUp • An 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.
LineUp • Vedio • http://www.youtube.com/watch?v=iFqCBI4T8ks
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.
