95 research outputs found
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/
Visual Guitar Tab Comparison
We designed a visual interface for comparing different guitar tablature (tab)
versions of the same piece. By automatically aligning the bars of these
versions and visually encoding different metrics, our interface helps determine
similarity, difficulty, and correctness. During our design, we collected and
integrated feedback from musicians and finally conducted a qualitative
evaluation with five guitarists. Results confirm that our interface effectively
supports comparison and helps musicians choose a version appropriate for their
personal skills and tastes.Comment: Late-breaking demo for ISMIR 2023
https://ismir2023program.ismir.net/lbd_357.htm
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
Toward Agile Situated Visualization: An Exploratory User Study
We introduce AVAR, a prototypical implementation of an agile situated
visualization (SV) toolkit targeting liveness, integration, and expressiveness.
We report on results of an exploratory study with AVAR and seven expert users.
In it, participants wore a Microsoft HoloLens device and used a Bluetooth
keyboard to program a visualization script for a given dataset. To support our
analysis, we (i) video recorded sessions, (ii) tracked users' interactions, and
(iii) collected data of participants' impressions. Our prototype confirms that
agile SV is feasible. That is, liveness boosted participants' engagement when
programming an SV, and so, the sessions were highly interactive and
participants were willing to spend much time using our toolkit (i.e., median >=
1.5 hours). Participants used our integrated toolkit to deal with data
transformations, visual mappings, and view transformations without leaving the
immersive environment. Finally, participants benefited from our expressive
toolkit and employed multiple of the available features when programming an SV.Comment: CHI '20 Extended Abstract
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