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.

Data science is integral. Its importance continues to grow, and so does the need for adequate tools to integrate multiple datasets and audit their transformations. In rapidly evolving fields where data formats frequently change, this task is often performed manually. However, manual data-wrangling tasks are often error-prone and time-consuming for human analysts. In this paper, we propose a semi-automatic data wrangling approach that allows analysts to interactively integrate heterogeneous structured datasets into a unified target data format. This is achieved by abstracting raw data into schemas and transforming relevant attributes into a target data schema. To assist analysts, it also suggests an initial transformation and visualizes the resulting data to verify the transformation. We also provide a use case to demonstrate the capabilities of our interface for wrangling and verification. Supplemental materials are available at https://osf.io/dscfb/?viewonly=7b50be799c8540eaaf50e5b296629530.

As data becomes more and more pervasive in our daily lives, supporting people in getting visual insights into their data is an important challenge to address. However, as data visualization literacy is still low, a gap between designers' mental model and users is not uncommon. To best pick up users where they are, we propose to include the data mental models of users into the design process. In this paper, we present our investigations in this direction by incorporating user sketches of their idea about their personal data stored at online services as a basis of our designs. We present our design study on personal data visualization interfaces resulting in a set of user sketches for three types of user groups and in three visualization interfaces. Finally, we reflect on our learnings and identify pitfalls to support other researchers in applying similar approaches.

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.

Online services are used for all kinds of activities, like news, entertainment, publishing content or connecting with others. But information technology enables new threats to privacy by means of global mass surveillance, vast databases and fast distribution networks. Current news are full of misuses and data leakages. In most cases, users are powerless in such situations and develop an attitude of neglect for their online behaviour. On the other hand, the GDPR (General Data Protection Regulation) gives users the right to request a copy of all their personal data stored by a particular service, but the received data is hard to understand or analyze by the common internet user. This paper presents TransparencyVis - a web-based interface to support the visual and interactive exploration of data exports from different online services. With this approach, we aim at increasing the awareness of personal data stored by such online services and the effects of online behaviour. This design study provides an online accessible prototype and a best practice to unify data exports from different sources.

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.