LineUp: Visual Analysis of Multi-Attribute Rankings

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.Engineering and Applied Science

Entourage: Visualizing Relationships between Biological Pathways using Contextual Subsets

Biological pathway maps are highly relevant tools for many tasks in molecular biology. They reduce the complexity of the overall biological network by partitioning it into smaller manageable parts. While this reduction of complexity is their biggest strength, it is, at the same time, their biggest weakness. By removing what is deemed not important for the primary function of the pathway, biologists lose the ability to follow and understand cross-talks between pathways. Considering these cross-talks is, however, critical in many analysis scenarios, such as judging effects of drugs. In this paper we introduce Entourage, a novel visualization technique that provides contextual information lost due to the artificial partitioning of the biological network, but at the same time limits the presented information to what is relevant to the analyst’s task. We use one pathway map as the focus of an analysis and allow a larger set of contextual pathways. For these context pathways we only show the contextual subsets, i.e., the parts of the graph that are relevant to a selection. Entourage suggests related pathways based on similarities and highlights parts of a pathway that are interesting in terms of mapped experimental data. We visualize interdependencies between pathways using stubs of visual links, which we found effective yet not obtrusive. By combining this approach with visualization of experimental data, we can provide domain experts with a highly valuable tool. We demonstrate the utility of Entourage with case studies conducted with a biochemist who researches the effects of drugs on pathways. We show that the technique is well suited to investigate interdependencies between pathways and to analyze, understand, and predict the effect that drugs have on different cell types.Engineering and Applied Science

Immersive Insights: A Hybrid Analytics System for Collaborative Exploratory Data Analysis

In the past few years, augmented reality (AR) and virtual reality (VR) technologies have experienced terrific improvements in both accessibility and hardware capabilities, encouraging the application of these devices across various domains. While researchers have demonstrated the possible advantages of AR and VR for certain data science tasks, it is still unclear how these technologies would perform in the context of exploratory data analysis (EDA) at large. In particular, we believe it is important to better understand which level of immersion EDA would concretely benefit from, and to quantify the contribution of AR and VR with respect to standard analysis workflows. In this work, we leverage a Dataspace reconfigurable hybrid reality environment to study how data scientists might perform EDA in a co-located, collaborative context. Specifically, we propose the design and implementation of Immersive Insights, a hybrid analytics system combining high-resolution displays, table projections, and augmented reality (AR) visualizations of the data. We conducted a two-part user study with twelve data scientists, in which we evaluated how different levels of data immersion affect the EDA process and compared the performance of Immersive Insights with a state-of-the-art, non-immersive data analysis system.Comment: VRST 201

Visually guiding users in selection, exploration, and presentation tasks

Making scientific discoveries based on large and heterogeneous datasets is challenging. The continuous improvement of data acquisition technologies makes it possible to collect more and more data. However, not only the amount of data is growing at a fast pace, but also its complexity. Visually analyzing such large, interconnected data collections requires a user to perform a combination of selection, exploration, and presentation tasks. In each of these tasks a user needs guidance in terms of (1) what data subsets are to be investigated from the data collection, (2) how to effectively and efficiently explore selected data subsets, and (3) how to effectively reproduce findings and tell the story of their discovery. On the basis of a unified model called the SPARE model, this thesis makes contributions to all three guidance tasks a user encounters during a visual analysis session: The LineUp multi-attribute ranking technique was developed to order and prioritize item collections. It is an essential building block of the proposed guidance process that has the goal of better supporting users in data selection tasks by scoring and ranking data subsets based on user-defined queries. Domino is a generic visualization technique for relating and exploring data subsets, supporting users in the exploration of interconnected data collections. Phoeva is a novel open-source visual analytics platform designed to speed up the creation of domain-specific exploration tools. The final building block of this thesis is CLUE, a universally applicable framework for capturing, labeling, understanding, and explaining visually driven exploration. Based on provenance data captured during the exploration process, users can author "Vistories", visual stories based on the history of the exploration. The practical applicability of the guidance model and visualization techniques developed is demonstrated by means of usage scenarios and use cases based on real-world data from the biomedical domain.submitted by DI Samuel Gratzl, BSc.Zusammenfassung in deutscher SpracheUniversität Linz, Dissertation, 2017OeBB(VLID)191700