MSCar: Enhancing Message Sequence Charts with Interactivity for Analysing (Automotive) Communication Sequences

Message Sequence Charts (MSCs) are a standardized and widespread form to visually describe interactions in distributed systems. Our approach proposes the enrichment of large scaled MSCs with novel interaction and design techniques used in the field of information visualization. Additionally, we show a graphical solution to visualize parallel, multi-directed communication processes in MSCs. Instead of the common application to specify system behaviours our interactive MSCs are aimed at exploring and diagnosing dependencies in network communication in general and, regarding our special requirements, within in-car communication traces. We implemented a prototype called MSCar with Focus and Context techniques, Dynamic Path Highlighting, Details on Demand and Colour Coding to support the users' cognitive abilities. A qualitative user study on MSCar gave us preliminary feedback and disclosed potentials of our approach

Design Patterns for Situated Visualization in Augmented Reality

Situated visualization has become an increasingly popular research area in the visualization community, fueled by advancements in augmented reality (AR) technology and immersive analytics. Visualizing data in spatial proximity to their physical referents affords new design opportunities and considerations not present in traditional visualization, which researchers are now beginning to explore. However, the AR research community has an extensive history of designing graphics that are displayed in highly physical contexts. In this work, we leverage the richness of AR research and apply it to situated visualization. We derive design patterns which summarize common approaches of visualizing data in situ. The design patterns are based on a survey of 293 papers published in the AR and visualization communities, as well as our own expertise. We discuss design dimensions that help to describe both our patterns and previous work in the literature. This discussion is accompanied by several guidelines which explain how to apply the patterns given the constraints imposed by the real world. We conclude by discussing future research directions that will help establish a complete understanding of the design of situated visualization, including the role of interactivity, tasks, and workflows.Comment: To appear in IEEE VIS 202

Visual Analysis of In-Car Communication Networks

Analyzing, understanding and working with complex systems and large datasets has become a familiar challenge in the information era. The explosion of data worldwide affects nearly every part of society, particularly the science, engineering, health, and financial domains. Looking, for instance at the automotive industry, engineers are confronted with the enormously increased complexity of vehicle electronics. Over the years, a large number of advanced functions, such as ACC (adaptive cruise control), rear seat entertainment systems or automatic start/stop engines, has been integrated into the vehicle. Thereby, the functions have been more and more distributed over the vehicle, leading to the introduction of several communication networks. Overlooking all relevant data facets, understanding dependencies, analyzing the flow of messages and tracking down problems in these networks has become a major challenge for automotive engineers. Promising approaches to overcome information overload and to provide insight into complex data are Information Visualization (InfoVis) and Visual Analytics (VA). Over the last decades, these research communities spent much effort on developing new methods to help users obtain insight into complex data. However, few of these solutions have yet reached end users, and moving research into practice remains one of the great challenges in visual data analysis. This situation is particularly true for large company settings, where very little is known about additional challenges, obstacles and requirements in InfoVis/VA development and evaluation. Users have to be better integrated into our research processes in terms of adequate requirements analysis, understanding practices and challenges, developing well-directed, user-centered technologies and evaluating their value within a realistic context. This dissertation explores a novel InfoVis/VA application area, namely in-car communication networks, and demonstrates how information visualization methods and techniques can help engineers to work with and better understand these networks. Based on a three-year internship with a large automotive company and the close cooperation with domain experts, I grounded a profound understanding of specific challenges, requirements and obstacles for InfoVis/VA application in this area and learned that “designing with not for the people” is highly important for successful solutions. The three main contributions of this dissertation are: (1) An empirical analysis of current working practices of automotive engineers and the derivation of specific design requirements for InfoVis/VA tools; (2) the successful application and evaluation of nine prototypes, including the deployment of five systems; and (3) based on the three-year experience, a set of recommendations for developing and evaluating InfoVis systems in large company settings. I present ethnographic studies with more than 150 automotive engineers. These studies helped us to understand currently used tools, the underlying data, tasks as well as user groups and to categorize the field into application sub-domains. Based on these findings, we propose implications and recommendations for designing tools to support current practices of automotive network engineers with InfoVis/VA technologies. I also present nine InfoVis design studies that we built and evaluated with automotive domain experts and use them to systematically explore the design space of applying InfoVis to in-car communication networks. Each prototype was developed in a user-centered, participatory process, respectively with a focus on a specific sub-domain of target users with specific data and tasks. Experimental results from studies with real users are presented, that show that the visualization prototypes can improve the engineers’ work in terms of working efficiency, better understanding and novel insights. Based on lessons learned from repeatedly designing and evaluating our tools together with domain experts at a large automotive company, I discuss challenges and present recommendations for deploying and evaluating VA/InfoVis tools in large company settings. I hope that these recommendations can guide other InfoVis researchers and practitioners in similar projects by providing them with new insights, such as the necessity for close integration with current tools and given processes, distributed knowledge and high degree of specialization, and the importance of addressing prevailing mental models and time restrictions. In general, I think that large company settings are a promising and fruitful field for novel InfoVis applications and expect our recommendations to be useful tools for other researchers and tool designers

Designing Situated Dashboards: Challenges and Opportunities

Situated Visualization is an emerging field that unites several areas - visualization, augmented reality, human-computer interaction, and internet-of-things, to support human data activities within the ubiquitous world. Likewise, dashboards are broadly used to simplify complex data through multiple views. However, dashboards are only adapted for desktop settings, and requires visual strategies to support situatedness. We propose the concept of AR-based situated dashboards and present design considerations and challenges developed over interviews with experts. These challenges aim to propose directions and opportunities for facilitating the effective designing and authoring of situated dashboards.Comment: To be presented at ISA: 2nd Workshop on Immersive and Situated Analytics @ ISMAR 202

Metaphorical Visualization: Mapping Data to Familiar Concepts

We present a new approach to visualizing data that is well-suitedfor personal and casual applications. The idea is to map the data toanother dataset that is already familiar to the user, and then relyon their existing knowledge to illustrate relationships in the data.We construct the map by preserving pairwise distances or by maintaining relative values of specific data attributes. This metaphoricalmapping is very flexible and allows us to adapt the visualization toits application and target audience. We present several exampleswhere we map data to different domains and representations. Thisincludes mapping data to cat images, encoding research interestswith neural style transfer and representing movies as stars in thenight sky. Overall, we find that although metaphors are not as accurate as the traditional techniques, they can help design engagingand personalized visualizations.<br/

Paraglide: Interactive Parameter Space Partitioning for Computer Simulations

In this paper we introduce paraglide, a visualization system designed for interactive exploration of parameter spaces of multi-variate simulation models. To get the right parameter configuration, model developers frequently have to go back and forth between setting parameters and qualitatively judging the outcomes of their model. During this process, they build up a grounded understanding of the parameter effects in order to pick the right setting. Current state-of-the-art tools and practices, however, fail to provide a systematic way of exploring these parameter spaces, making informed decisions about parameter settings a tedious and workload-intensive task. Paraglide endeavors to overcome this shortcoming by assisting the sampling of the parameter space and the discovery of qualitatively different model outcomes. This results in a decomposition of the model parameter space into regions of distinct behaviour. We developed paraglide in close collaboration with experts from three different domains, who all were involved in developing new models for their domain. We first analyzed current practices of six domain experts and derived a set of design requirements, then engaged in a longitudinal user-centered design process, and finally conducted three in-depth case studies underlining the usefulness of our approach

Human-centered machine learning through interactive visualization

The goal of visual analytics (VA) systems is to solve complex problems by integrating automated data analysis methods, such as machine learning (ML) algorithms, with interactive visualizations. We propose a conceptual framework that models human interactions with ML components in the VA process, and makes the crucial interplay between automated algorithms and interactive visualizations more concrete. The framework is illustrated through several examples. We derive three open research challenges at the intersection of ML and visualization research that will lead to more effective data analysis

Visual interaction with dimensionality reduction: a structured literature analysis

Dimensionality Reduction (DR) is a core building block in visualizing multidimensional data. For DR techniques to be useful in exploratory data analysis, they need to be adapted to human needs and domain-specific problems, ideally, interactively, and on-the-fly. Many visual analytics systems have already demonstrated the benefits of tightly integrating DR with interactive visualizations. Nevertheless, a general, structured understanding of this integration is missing. To address this, we systematically studied the visual analytics and visualization literature to investigate how analysts interact with automatic DR techniques. The results reveal seven common interaction scenarios that are amenable to interactive control such as specifying algorithmic constraints, selecting relevant features, or choosing among several DR algorithms. We investigate specific implementations of visual analysis systems integrating DR, and analyze ways that other machine learning methods have been combined with DR. Summarizing the results in a “human in the loop” process model provides a general lens for the evaluation of visual interactive DR systems. We apply the proposed model to study and classify several systems previously described in the literature, and to derive future research opportunities