Abstract The analysis of state transition sequences is a prevalent research topic in many domains. In this context, we introduce the term higher‐order property to describe characteristics of the analyzed data set that span beyond the local neighborhood of a single state. For the analysis of such properties, we elaborate why sequence diagrams are generally preferred over node‐link diagrams in the literature. Consequently, we provide an overview of existing adaptations to node‐link diagrams to trade‐off support for first‐order property analysis in favor of higher‐order property analysis, potentially combining the strengths of both visualization types. To better understand the impact of this tradeoff, we present a comparative study of static sequence diagrams and spline‐based node‐link diagrams for five perception tasks on small‐scale synthetic state transition sequence data. We focus on static stimuli of small‐scale data sets to model the post‐filtering perceptual process rather than an end‐to‐end analytical workflow. The study confirmed hypotheses regarding the superior user performance with the sequence diagram for the perception of higher‐order properties. To demonstrate the relevance of higher‐order property analysis for real‐world problems, we present an application scenario from the cybersecurity domain. Based on the results of our study, we apply a sequence diagram‐based prototype to this application scenario. All supplemental materials are available at https://osf.io/r4ycd/ .

Vulnerability verification is an important process in ensuring the security of software systems. To support users in this process, we present the design study of DaV$^{3}$3is, which utilizes visual event sequence analysis techniques to enable the comparison and tracing of automatically detected data flows through the software's source code, thereby allowing users to take advantage of sequence similarities to reduce the verification workload. To that end, we characterize the domain problem based on input from domain users, describe our design rationale based on best-practices from the visual analytics literature, and evaluate individual design decisions, usability, and utility in studies with three stakeholder groups. The evaluations yielded overall positive responses, showing the suitability of our design and providing valuable insight for future research.

Radial visualizations encode one dimension of a data set with the visual variable angle or arc length. The radial axis conveys a similarity between the positions of the data points, due to the law of proximity. In the literature, two different types of axes are employed in visualizations: open and closed circular axes. Open axes are linear axes bent to form an open circle. A gap between the right and left poles indicates dissimilarity of data points at both poles. Contrarily, closed axes form a full circle. While the choice of the appropriate axis type in the visualization should align with the similarity space, a question arises: Which axis type supports the human perception of proximity better? To answer this question, we conducted a quantitative task-driven experiment (N=28) to evaluate human distance perception on open and closed circular axes. Within four low-level tasks of three types (identify, compare, and summarize), we evaluate accuracy and response time. Based on our results, we provide an empirically grounded guideline for selecting the appropriate axis type. In our extensive post-hoc analysis, we gain preliminary, but valuable insights to inform further research.

To improve the perception of hierarchical structures in data sets, several color map generation algorithms have been proposed to take this structure into account. But the design of hierarchical color maps elicits different requirements to those of color maps for tabular data. Within this paper, we make an initial effort to put design rules from the color map literature into the context of hierarchical color maps. We investigate the impact of several design decisions and provide recommendations for various analysis scenarios. Thus, we lay the foundation for objective quality criteria to evaluate hierarchical color maps.

Today’s software systems are created by software development processes that naturally include mistakes, some of which can be exploited by attackers and are therefore called vulnerabilities. Automatic software scanners enable developers to analyze their applications to detect vulnerabilities and alert them of their presence. But often these reports are hard to understand, include false positives or overwhelm users due to the sheer number of alerts, since a report may contain hundreds to thousands of vulnerabilities. Developers must undergo a process called vulnerability triage to find the relevant vulnerabilities to fix. This paper presents two interactive visualizations for developers and security experts to gain an overview of the security state of their application. Users can see the distribution of vulnerabilities, find the most relevant ones, and compare differences between application versions. Our visualization design is inspired by an initial preliminary study and has been evaluated by domain experts to investigate the usability and appropriateness.

With the aim to increase the awareness of the everyday internet user for the own home network traffic, we present two interactive visualization interfaces for visual exploration of home router traffic records. Thereby we differentiate between users with a present intrinsic motivation for the topic and those with absent intrinsic motivation. Therefore, gamification in the first interface is used to maintain motivation of the first type of user, while the storytelling concept based on the hero's journey in the second interface aims at increasing the perceived incentives for the second user group.