Cardiogram

Abstract

We present Cardiogram, a visual analytics system that supports automotive engineers in debugging masses of traces each consisting of millions of recorded messages from in-car communication networks. With their increasing complexity, ensuring these safety-critical networks to be error-free has become a major task and challenge for automotive engineers. To overcome shortcomings of current analysis tools, Cardiogram combines visualization techniques with a data preprocessing approach to automatically reduce complexity based on engineers' domain knowledge. In this paper, we provide the findings from an exploratory, three-year field study within a large automotive company, studying current practices of engineers, the challenges they meet and the characteristics for integrating novel visual analytics tools into their work practices. We then introduce Cardiogram, discuss how our field analysis influenced our design decisions, and present a qualitative, long-term, in-depth evaluation. Results of this study showed that our participants successfully used Cardiogram to increase the amount of analyzable information, to externalize domain knowledge, and to provide new insights into trace data. Our design approach finally led to the adoption of Cardiogram into engineers' daily practices.

Information visualization evaluation in large companies: Challenges, experiences and recommendations

“…We designed, implemented and evaluated nine information visualization systems for them resulting in five integrated and adopted systems which are still used by engineers in their daily work practices; Figure 1 gives a temporal overview over all nine projects including the evaluation methods we used; additional details on some of our projects can be found in other publications. [38][39][40][41][42] Before, during and after developing these tools, we applied a variety of evaluation techniques in order to better understand problems, practices and requirements (pre-design), to learn how to improve our tool designs (during-design), and to validate the domain value of our own, novel approaches (post-design). In the following, we present two case studies from our own work in order to illustrate the challenges we encountered as well as our approaches to overcome them.…”

ManuKnowVis: How to Support Different User Groups in Contextualizing and Leveraging Knowledge Repositories

“…There, design studies and resulting VA applications mainly support product design, condition monitoring of stations, the optimization of testing procedures, or the visual support of high cognition tasks. Efforts were carried out to visualize in-car communication networks [41], [42], [43], to facilitate the exploration of multi-criteria alternatives for rotor designs [7],to detect and analyze anomalies in test stations [14], [50], the visual exploration of assembling data to detect inefficiencies [54], and to support mechanical engineers in the analysis of acoustic signatures of electrical engines [12]. Some of the mentioned studies explicitly acknowledge the need for externalizing tacit expert knowledge [14].…”

“…Some of the mentioned studies explicitly acknowledge the need for externalizing tacit expert knowledge [14]. For example, the Cardiogram system [43] stores externalized expert knowledge in the form of state machine diagrams, while IRVINE [12] stores expert knowledge in the form of labels for electric engines and annotations in the raw sensor data. All mentioned studies succeeded in creating insights for engineering experts based on machine sensor data.…”

ICEBOAT: An Interactive User Behavior Analysis Tool for Automotive User Interfaces

“…Even though automotive UX professionals express clear needs for effective visualizations to support their work, there is not much research on big data analytics to evaluate user interactions with IVIS. Most visual analytics approaches in the automotive domain focus on visualizing data collected from a few sensors [45] or in controlled experimental studies [27]. For example, Jansen et al [27] present an approach to visualize spatiotemporal data collected during user interface interaction studies.…”