RelEx: Visualization for Actively Changing Overlay Network Specifications

ManEx: The Visual Analysis of Measurements for the Assessment of Errors in Electrical Engines

Electrical engines are a key technology all automotive manufacturers must master to stay competitive. Engineers need to analyze an overwhelming number of engine measurements to improve the manufacturing for this technology. They are hindered in the task of analyzing large numbers of engines, however, by the following challenges: 1) Engines comprise a complex hierarchical structure of subcomponents. 2) Locating the cause of errors along manufacturing processes is a difficult procedure. 3) Large numbers of heterogeneous measurements impair the ability to explain errors in engines. We address these challenges in a design study with automotive engineers and by developing the visual analytics system Manufacturing Explorer (ManEx), which provides interactive interfaces to analyze measurements of engines across the manufacturing process. ManEx was validated by five experts. Our results suggest high usability and usefulness scores and the improvement of a real-world manufacturing process. Specifically, with ManEx, experts reduced scraped parts by over 3%.

IRVINE: A Design Study on Analyzing Correlation Patterns of Electrical Engines

In this design study, we present IRVINE, a Visual Analytics (VA) system, which facilitates the analysis of acoustic data to detect and understand previously unknown errors in the manufacturing of electrical engines. In serial manufacturing processes, signatures from acoustic data provide valuable information on how the relationship between multiple produced engines serves to detect and understand previously unknown errors. To analyze such signatures, IRVINE leverages interactive clustering and data labeling techniques, allowing users to analyze clusters of engines with similar signatures, drill down to groups of engines, and select an engine of interest. Furthermore, IRVINE allows to assign labels to engines and clusters and annotate the cause of an error in the acoustic raw measurement of an engine. Since labels and annotations represent valuable knowledge, they are conserved in a knowledge database to be available for other stakeholders. We contribute a design study, where we developed IRVINE in four main iterations with engineers from a company in the automotive sector. To validate IRVINE, we conducted a field study with six domain experts. Our results suggest a high usability and usefulness of IRVINE as part of the improvement of a real-world manufacturing process. Specifically, with IRVINE domain experts were able to label and annotate produced electrical engines more than 30% faster.

SafetyLens: Visual Data Analysis of Functional Safety of Vehicles

Modern automobiles have evolved from just being mechanical machines to having full-fledged electronics systems that enhance vehicle dynamics and driver experience. However, these complex hardware and software systems, if not properly designed, can experience failures that can compromise the safety of the vehicle, its occupants, and the surrounding environment. For example, a system to activate the brakes to avoid a collision saves lives when it functions properly, but could lead to tragic outcomes if the brakes were applied in a way that's inconsistent with the design. Broadly speaking, the analysis performed to minimize such risks falls into a systems engineering domain called Functional Safety. In this paper, we present SafetyLens, a visual data analysis tool to assist engineers and analysts in analyzing automotive Functional Safety datasets. SafetyLens combines techniques including network exploration and visual comparison to help analysts perform domain-specific tasks. This paper presents the design study with domain experts that resulted in the design guidelines, the tool, and user feedback.

Visilant: Visual Support for the Exploration and Analytical Process Tracking in Criminal Investigations

The daily routine of criminal investigators consists of a thorough analysis of highly complex and heterogeneous data of crime cases. Such data can consist of case descriptions, testimonies, criminal networks, spatial and temporal information, and virtually any other data that is relevant for the case. Criminal investigators work under heavy time pressure to analyze the data for relationships, propose and verify several hypotheses, and derive conclusions, while the data can be incomplete or inconsistent and is changed and updated throughout the investigation, as new findings are added to the case. Based on a four-year intense collaboration with criminalists, we present a conceptual design for a visual tool supporting the investigation workflow and Visilant, a web-based tool for the exploration and analysis of criminal data guided by the proposed design. Visilant aims to support namely the exploratory part of the investigation pipeline, from case overview, through exploration and hypothesis generation, to the case presentation. Visilant tracks the reasoning process and as the data is changing, it informs investigators which hypotheses are affected by the data change and should be revised. The tool was evaluated by senior criminology experts within two sessions and their feedback is summarized in the paper. Additional supplementary material contains the technical details and exemplary case study.

DIA2: Web-based Cyberinfrastructure for Visual Analysis of Funding Portfolios

We present a design study of the Deep Insights Anywhere, Anytime (DIA2) platform, a web-based visual analytics system that allows program managers and academic staff at the U.S. National Science Foundation to search, view, and analyze their research funding portfolio. The goal of this system is to facilitate users' understanding of both past and currently active research awards in order to make more informed decisions of their future funding. This user group is characterized by high domain expertise yet not necessarily high literacy in visualization and visual analytics-they are essentially casual experts-and thus require careful visual and information design, including adhering to user experience standards, providing a self-instructive interface, and progressively refining visualizations to minimize complexity. We discuss the challenges of designing a system for casual experts and highlight how we addressed this issue by modeling the organizational structure and workflows of the NSF within our system. We discuss each stage of the design process, starting with formative interviews, prototypes, and finally live deployments and evaluation with stakeholders.