相关论文: Mod2Dash: A Framework for Model-Driven Dashboards …
Configuring and evolving dashboards in complex and large-scale Systems-of-Systems (SoS) can be an expensive and cumbersome task due to the many Key Performance Indicators (KPIs) that are usually collected and have to be arranged in a number…
Industrial dashboards, commonly deployed by organizations such as enterprises and governments, are increasingly crucial in data communication and decision-making support across various domains. Designing an industrial dashboard prototype is…
Systems of systems are highly dynamic software systems that require flexible monitoring solutions to be observed and controlled. Indeed, operators have to frequently adapt the set of collected indicators according to changing circumstances,…
This paper introduces design patterns for dashboards to inform dashboard design processes. Despite a growing number of public examples, case studies, and general guidelines there is surprisingly little design guidance for dashboards. Such…
While Large Language Models (LLMs) have demonstrated remarkable proficiency in generating standalone charts, synthesizing comprehensive dashboards remains a formidable challenge. Existing end-to-end paradigms, which typically treat…
Dashboard guidance helps dashboard users better navigate interactive features, understand the underlying data, and assess insights they can potentially extract from dashboards. However, authoring dashboard guidance is a time consuming task,…
Adapting dashboard design to different contexts of use is an open question in visualisation research. Dashboard designers often seek to strike a balance between dashboard adaptability and ease-of-use, and in hospitals challenges arise from…
Organizations of all kinds, whether public or private, profit-driven or non-profit, and across various industries and sectors, rely on dashboards for effective data visualization. However, the reliability and efficacy of these dashboards…
Recent progress in robot learning has been driven by large-scale datasets and powerful visuomotor policy architectures, yet policy robustness remains limited by the substantial cost of collecting diverse demonstrations, particularly for…
Autonomous driving technology has witnessed rapid advancements, with foundation models improving interactivity and user experiences. However, current autonomous vehicles (AVs) face significant limitations in delivering command-based driving…
Despite the success of vision-language models in various generative tasks, obtaining high-quality semantic representations for products and user intents is still challenging due to the inability of off-the-shelf models to capture nuanced…
In the last few years, Model Driven Development (MDD), Component-based Software Development (CBSD), and context-oriented software have become interesting alternatives for the design and construction of self-adaptive software systems. In…
We present a sketch-based CAD modeling system, where users create objects incrementally by sketching the desired shape edits, which our system automatically translates to CAD operations. Our approach is motivated by the close similarities…
Professional designers work from client briefs that specify goals and constraints but often lack concrete design details. Translating these abstract requirements into visual designs poses a central challenge, yet existing tools address…
Analytical dashboards are popular in business intelligence to facilitate insight discovery with multiple charts. However, creating an effective dashboard is highly demanding, which requires users to have adequate data analysis background…
Generative systems have a significant potential to synthesize innovative design alternatives. Still, most of the common systems that have been adopted in design require the designer to explicitly define the specifications of the procedures…
Domain-driven design (DDD) is a powerful design technique for architecting complex software systems. This paper introduces a prompting framework that automates core DDD activities through structured large language model (LLM) interactions.…
Safely interacting with humans is a significant challenge for autonomous driving. The performance of this interaction depends on machine learning-based modules of an autopilot, such as perception, behavior prediction, and planning. These…
In recent years, a large number of research efforts aimed at the development of machine learning models to predict complex spatial-temporal mobility patterns and their impact on road traffic and infrastructure. However, the utility of these…
Despite their ubiquity, authoring dashboards for metrics reporting in modern data analysis tools remains a manual, time-consuming process. Rather than focusing on interesting combinations of their data, users have to spend time creating…