LineUp: Visual Analysis of Multi-Attribute Rankings

Abstract

Rankings are a popular and universal approach to structuring otherwise unorganized collections of items by computing a rank for each item based on the value of one or more of its attributes. This allows us, for example, to prioritize tasks or to evaluate the performance of products relative to each other. While the visualization of a ranking itself is straightforward, its interpretation is not, because the rank of an item represents only a summary of a potentially complicated relationship between its attributes and those of the other items. It is also common that alternative rankings exist which need to be compared and analyzed to gain insight into how multiple heterogeneous attributes affect the rankings. Advanced visual exploration tools are needed to make this process efficient. In this paper we present a comprehensive analysis of requirements for the visualization of multi-attribute rankings. Based on these considerations, we propose LineUp - a novel and scalable visualization technique that uses bar charts. This interactive technique supports the ranking of items based on multiple heterogeneous attributes with different scales and semantics. It enables users to interactively combine attributes and flexibly refine parameters to explore the effect of changes in the attribute combination. This process can be employed to derive actionable insights as to which attributes of an item need to be modified in order for its rank to change. Additionally, through integration of slope graphs, LineUp can also be used to compare multiple alternative rankings on the same set of items, for example, over time or across different attribute combinations. We evaluate the effectiveness of the proposed multi-attribute visualization technique in a qualitative study. The study shows that users are able to successfully solve complex ranking tasks in a short period of time.

Using Gap Charts to Visualize the Temporal Evolution of Ranks and Scores

“…RC are also extensively used on the web 6 , and come with many variations. While RC clearly show changes in rank, they do not show the magnitudes of score differences, nor do they show tied entries-an important feature according to [5]. That said, some designs have attempted to address the latter limitation by grouping entries at a same rank on the ordinal axis 7 .…”

“…RankExplorer [6], which uses stacked area charts, and LineUp [5], which enables interactive analysis of multidimensional ranked entries, were developed to visualize timedependent rankings. Although they are powerful tools for performing advanced queries, their visual and interaction complexity is high, and we believe untrained people may find it difficult to interpret and interact with them.…”

Visual Soccer Analytics: Understanding the Characteristics of Collective Team Movement Based on Feature-Driven Analysis and Abstraction

“…A system called Line-Up is introduced by Gratzl et al [25], where bar charts are used to derive a scalable and efficient visualization technique to interactively explore, analyze and compare multi-attribute rankings with different parameters. A technique to visualize sport ranking evolution is presented by Perin et al [26], where a hybrid version of rank chart and slope graph visualizations positions the teams with respect to their performance and time during a season.…”

Knowledge-Assisted Ranking: A Visual Analytic Application for Sports Event Data

“…Event ranking (e.g., for determining relevance or prioritizing actions) is an important task in visual analytics [6,8]. This task becomes challenging when sorting involves several data dimensions, and the way in which each dimension influences the sorting is not well defined.…”