Open Access

A digital twin (DT) plays a vital role in the advancement of manufacturers towards Industry 4.0. However, the creation and maintenance of DTs can be time-consuming. One approach to streamline this process is the utilization of process mining (PM) methods and techniques, which can automatically generate valuable information for DTs. Therefore, this paper aims to examine different approaches that augment DTs with PM and explore their effects. The review categorizes these approaches into three groups: theoretical approaches, approaches with laboratory case studies, and approaches with real-world case studies conducted by manufacturers. The review reveals that the use of PM can enhance the flexibility and sustainability of DTs. However, this improvement comes at the cost of requiring high-quality data and more data preparation efforts.

The initial idea of Robotic Process Automation (RPA) is the automation of business processes through the presentation layer of existing application systems. For this simple emulation of user input and output by software robots, no changes of the systems and architecture is required. However, considering strategic aspects of aligning business and technology on an enterprise level as well as the growing capabilities of RPA driven by artificial intelligence, interrelations between RPA and Enterprise Architecture (EA) become visible and pose new questions. In this paper we discuss the relationship between RPA and EA in terms of perspectives and implications. As work in-progress we focus on EA.

Networked sensors are strategically deployed to gather real-world data, thereby facilitating the implementation of cutting-edge technologies like the Internet of Things (IoT) and Cyber Physical Systems (CPS). These devices, tailored to specific use-cases, often operate under strict resource constraints and must function optimally for extended periods. Concurrently, the sensitive data they collect must be safeguarded against unauthorized access, necessitating robust security measures. A range of security mechanisms is available to IoT system designers, with cryptographic algorithms, such as symmetric and asymmetric encryption, and hash functions being the cornerstone. They can select from multiple algorithm implementations and adjust configuration parameters to suit the device’s capabilities. However, determining the ideal configuration is a complex task, requiring a balance between security effectiveness and efficient resource utilization. While the security aspect is continually assessed by experts, the impact of these choices on resource consumption, particularly across diverse platforms, often receives less attention. This paper introduces an objective evaluation method that assesses not only the effect of different security algorithms, but also various implementations and configuration adjustments on the resource consumption across different platforms. To facilitate this, a composite performance indicator is computed, enabling the systematic ranking of candidate configurations. The approach bridges the gap in understanding the interplay between security measures and resource management in the realm of IoT devices.