Practice & Research

Research Topics in the HCC Program

Research in the Human-Centered Computing program focuses, on the one hand, on the interactive visualization of large amounts of data. The successful use of “big data” requires an intuitive user interface. Software developers and consulting firms in this field generally lack sufficient experience with the user-centered and cognitively optimal design of interactive visualizations for PCs, mobile devices, and large, high-resolution displays, and need better support in this regard.

The second research area encompasses human-centered industrial workplaces in the context of Industry 4.0. Here, the goal is to design universal methods and systems for the digital assistance of production workers in the workplace and to ensure safe human-machine interaction through sensor-based situation and status detection. These training and assistance measures—designed to improve quality and efficiency even for the smallest batch sizes—range from product-specific work instructions on a screen to augmented reality systems and collaborative robots in the workplace.

Research Groups and Researchers

Research Groups:

• Assistive Technology Lab
• HIVE - Human Interfaces & Virtual Environments
• PEEC - Personalized Environments and Collaborative Systems

Researchers:

Werner Christian Kurschl
Mandy Keck

R&D Projects

DIH.work - Digital Innovation Hub WORKPLACE SMEs

The Digital Innovation Hub WORKPLACE SMEs promotes future-oriented and sustainable work environments in Austrian SMEs within a digital society. Through collaboration between research, government, advocacy groups, and innovators, a dynamic cycle emerges that involves understanding the needs of SMEs, mobilizing and informing stakeholders, and effectively implementing digital innovations in, at, and with Austrian SMEs.

Details: DIH.work – Digital Innovation Hub WORKPLACE SMEs

IVES – Interactive Visualizations for Recommendation Systems

To address the problem of ever-growing data volumes and the associated information overload, and to reduce data to a manageable level, recommendation systems are increasingly being used in online services and information systems. However, such systems often appear as “black boxes”: it is difficult to understand how recommendations are generated or why a specific list of results is presented. Interactive visualizations can provide support at various levels to increase user trust and improve the user experience (UX). As part of the IVES project, a modular interface toolkit for interactive visualizations in recommendation systems, a catalog of design patterns, and an exploration and analysis tool are being developed.