Efficient monitoring of machine downtimes and order throughput is critical for productivity in metal manufacturing. Existing shopfloor management boards are often either too detailed or too superficial, and lack adaptability to user needs and cognitive capacities. These limitations hinder operators’ and managers’ ability to detect and respond to unplanned downtimes in a timely manner. Therefore, we iteratively designed a near-real-time shopfloor board visualisation supporting users in efficiently identifying machine downtimes. We closely collaborated with a metal manufacturer over a couple of months to develop a tailored solution using an iterative user-centered design process, including semi-structured interviews, low- and high-fidelity prototyping, cognitive walkthroughs with experts, and a task-based usability study. The final shopfloor board visualisation adopts a card-based layout that presents key machine-level metrics such as throughput, order list, and production counts. Our findings of the user-centered design process show that clear hierarchies, intuitive colour coding, and minimalism can enhance shopfloor board visualisation design.

Long lead times in supply chain processes can result in significant tied-up working capital in inventory, limiting operational flexibility and profitability. To address this challenge, we present VAILO, a Visual Analytics Dashboard for Inventory and Logistics Operations, designed to support sales and operations managers in identifying inefficiencies and bottlenecks in supply chain workflows. Developed through a co-design process with domain experts, VAILO integrates two coordinated visualizations—a hierarchical treemap and a throughput diagram—to facilitate the exploration of material flow and lead time variability across industries and customers. The dashboard enables secure data uploads, customizable visual encodings, and interactive filtering to support user-driven analysis. We conducted a formative usability study with participants experienced in industrial contexts and explored a real-world usage scenario. While initial results suggest that VAILO may help surface actionable insights, they also highlight areas for improvement in onboarding and interaction design. Our findings offer a preliminary contribution toward the design of domain-specific visual analytics tools that aim to bridge the gap between complex data and decision-making in supply chain contexts.

A typical problem in Visual Analytics (VA) is that users are highly trained experts in their application domains, but have mostly no experience in using VA systems. Thus, users often have difficulties interpreting and working with visual representations. To overcome these problems, user assistance can be incorporated into VA systems to guide experts through the analysis while closing their knowledge gaps. Different types of user assistance can be applied to extend the power of VA, enhance the user’s experience, and broaden the audience for VA. Although different approaches to visualization onboarding and guidance in VA already exist, there is a lack of research on how to design and integrate them in effective and efficient ways. Therefore, we aim at putting together the pieces of the mosaic to form a coherent whole. Based on the Knowledge-Assisted Visual Analytics model, we contribute a conceptual model of user assistance for VA by integrating the process of visualization onboarding and guidance as the two main approaches in this direction. As a result, we clarify and discuss the commonalities and differences between visualization onboarding and guidance, and discuss how they benefit from the integration of knowledge extraction and exploration. Finally, we discuss our descriptive model by applying it to VA tools integrating visualization onboarding and guidance, and showing how they should be utilized in different phases of the analysis in order to be effective and accepted by the user.

Air traffic control officers (ATCOs) are specialized workers responsible to monitor and guide airplanes in their assigned airspace. Such a task is highly visual and mainly supported by 2D visualizations. In this paper, we designed and assessed an application for visualizing air traffic in both orthographic (2D) and perspective (3D) views. A user study was then performed to compare these two types of representations in terms of situation awareness, workload, performance, and user acceptance. Results show that the 3D view yielded both higher situation awareness and less workload than the 2D view condition. However, such a performance does not match the opinion of the ATCOs about the 3D representation.

In the course of further digitization of industrial manufacturing, companies are using tracking systems to monitor their production processes. This creates a large amount of data with location references, having an influence on the employees privacy - especially if personal references to employees are possible. To investigate this topic, a use case was carried out in an industrial environment. Therefore, a self-developed asset tracking (AT) system was developed and compared with a Commercial-of-the-shelf (COTS) AT-System with regard to functionality and employee privacy. One mayor finding was that the Bluetooth Low Energy (BLE) based legacy system has weaknesses with regard to privacy, e.g. the advertising-data of mobile devices of the employees were stored in the cloud application of the system provider. This data can be used to determine the position of employees. With the self-developed system these weaknesses were avoided by using a Privacy by Design (PbD) approach. Therefore, this paper focuses on employee privacy in industrial AT applications and provides a PbD concept for wireless sensor networks in industrial environments.

In 2014, more than 10 million people in the US were affected by an ambulatory disability. Thus, gait rehabilitation is a crucial part of health care systems. The quantification of human locomotion enables clinicians to describe and analyze a patient's gait performance in detail and allows them to base clinical decisions on objective data. These assessments generate a vast amount of complex data which need to be interpreted in a short time period. We conducted a design study in cooperation with gait analysis experts to develop a novel Knowledge-Assisted Visual Analytics solution for clinical Gait analysis (KAVAGait). KAVAGait allows the clinician to store and inspect complex data derived during clinical gait analysis. The system incorporates innovative and interactive visual interface concepts, which were developed based on the needs of clinicians. Additionally, an explicit knowledge store (EKS) allows externalization and storage of implicit knowledge from clinicians. It makes this information available for others, supporting the process of data inspection and clinical decision making. We validated our system by conducting expert reviews, a user study, and a case study. Results suggest that KAVAGait is able to support a clinician during clinical practice by visualizing complex gait data and providing knowledge of other clinicians.

Mobile devices are more and more used in parallel, esp. in the field of TV viewing as second screen devices. Such scenarios aim to enhance the viewers' user experience while watching TV. We designed and implemented a second screen prototype intended to be used in parallel to watching a TV documentary. It allows to interactively explore a combination of spatial and time-oriented data to extend and enrich the TV content. We evaluated our proto-type in a twofold approach, consisting of expert reviews and user evaluation. We identified different interaction habits in a second screen scenario and present its benefits in relation to documentaries.

IT-security experts engage in behavior-based malware analysis in order to learn about previously unknown samples of malicious software (malware) or malware families. For this, they need to find and categorize suspicious patterns from large collections of execution traces. Currently available systems do not meet the analysts' needs which are described as: visual access suitable for complex data structures, visual representations appropriate for IT-security experts, provision of workflow-specific interaction techniques, and the ability to externalize knowledge in the form of rules to ease the analysis process and to share with colleagues. To close this gap, we designed and developed KAMAS, a knowledge-assisted visualization system for behavior-based malware analysis. This paper is a design study that describes the design, implementation, and evaluation of the prototype. We report on the validation of KAMAS with expert reviews, a user study with domain experts and focus group meetings with analysts from industry. Additionally, we reflect on the acquired insights of the design study and discuss the advantages and disadvantages of the applied visualization methods. An interesting finding is that the arc-diagram was one of the preferred visualization techniques during the design phase but did not provide the expected benefits for finding patterns. In contrast, the seemingly simple looking connection line was described as supportive in finding the link between the rule overview table and the rule detail table which are playing a central role for the analysis in KAMAS.

Today many different devices and operating systems can be used for InfoVis systems. On the one hand, web-based visualizations can be used to be compatible with several systems, but the performance depends on optimized browser engines. On the other hand, it is possible to build a native system which supports all the benefits for just one device. However, transferring the code to another system means parts of the code or the programming language have to be adapted. To close this gap, we present a proof of concept based on the Unity3D game engine. We implemented a prototype following the InfoVis reference model and basic interactions for interactive data exploration. A major advantage is that we have now the ability to deploy native code to over 20 different devices. Additionally, this proof of concept opens new possibilities for a future InfoVis framework which benefits from Unity3D.

Behavior-based analysis of emerging malware families involves finding suspicious patterns in large collections of execution traces. This activity cannot be automated for previously unknown malware families and thus malware analysts would benefit greatly from integrating visual analytics methods in their process. However existing approaches are limited to fairly static representations of data and there is no systematic characterization and abstraction of this problem domain. Therefore we performed a systematic literature study, conducted a focus group as well as semi-structured interviews with 10 malware analysts to elicit a problem abstraction along the lines of data, users, and tasks. The requirements emerging from this work can serve as basis for future design proposals to visual analytics-supported malware pattern analysis.