Process Science
The research group has established a strong foundation in process science, with particular emphasis on integrating emerging technologies such as IoT, edge computing, and large language models into business process management (BPM). A central theme is the enhancement of process modeling and execution through context-awareness—especially location-aware BPM—as seen in developments like LABPMN and the TRADEmark framework. The group also investigates predictive and declarative process monitoring, process mining on industrial data (e.g., OPC UA), and the ethical dimensions of BPM. Collaborative research has resulted in frameworks and taxonomies to assess process maturity and readiness for digital transformation, alongside contributions to hybrid modeling languages and small-sample learning in predictive monitoring. A recurring interest lies in bridging the gap between BPM theory and practical applications, ensuring real-world relevance through reflective and applied research outputs.
Cyber Security Management in the Industrial IoT
Another key research pillar focuses on cybersecurity management in Industrial IoT (IIoT) environments, approached from a process-oriented perspective. The group addresses the complexities of IIoT security by developing models, metamodels, and guidelines for compliance monitoring, standard adherence, and security-aware process design. Research initiatives like SIREN and various literature reviews establish a systematic foundation for integrating security requirements into BPM systems. The group has also proposed strategies for ensuring traceability and accountability through real-time data analytics and security compliance verification mechanisms. Recent work extends this by incorporating ethical and governance considerations, reflecting a holistic approach to secure IIoT systems that combines technical innovation with organizational and regulatory awareness.
AI-based Analytics for Event-Driven Information Systems: Event Querying and Processing
The world is increasingly linked through a large number of connected devices, typically embedded in electrical/electronical components and equipped with sensors and actuators, that enable sensing, (re-)acting, collecting and exchanging data via various communication networks including the Internet of Things (IoT). As such, it enables continuous monitoring of phenomena based on sensing devices (wearable devices, beacons, smartphones, machine sensors, etc.) as well as analytics opportunities in smart environments (smart homes, connected cars, smart logistics, Industry 4.0, etc.). Event processing focuses on capturing and processing events with minimum latency, i.e., near real-time, for detecting changes or trends indicating opportunities or problems. In the context of dynamic systems like process-oriented information systems, events may represent state changes of objects. Complex event processing (CEP) comprises a set of techniques for making sense of the behavior of a monitored system by deriving higher level knowledge from lower level system events in a timely and online fashion. We have investigated the feasibility of applying the concept of event processing to process-oriented information systems in order to allow for closed-loop monitoring and control of processes within IoT environments. We developed an architecture for integrating information systems and event processing systems in a closed monitoring and control loop through the exchange of (complex) events. The proposed techniques have been implemented and extensively evaluated on several real world case studies in digital production environments.
The second stream of research in this field can be summarized under the term physical analytics. It aims at optimizing spatially distributed processing steps in the manufacturing sector. The goal is to develop directly applicable and AI-based tools and methods for physical production processes with high variant diversity and flexibility. Recently, we were devising predictive techniques for the derivation of forecasts, i.e., predictive maintenance techniques in the context of distributed, event-driven production information systems by applying different classification and regression methods consisting of several uncorrelated decision trees and different settings of artificial neural networks.
AI-based Analytics for Process-Oriented Information Systems: Process Mining and Operational Decision Support
The application of measurement and analysis techniques on event data from interaction systems has been proposed under the terms process mining and process analytics. Process mining is an approach at the intersection of model-driven engineering and data science, whose purpose is to analyse the event data generated through the execution of processes to obtain insights on how processes are executed in reality, and enable continuous improvement based on facts. Our contributions in this area can be grouped along three directions. First of all, while a large share of process mining focuses on automated discovery of imperative process models from event data, we contributed to create the sub-field of declarative process discovery, whose main goal is to extract rules from event data.
The proposed techniques have been tested on several real world case studies. Second, we study how to predict processes based on deep learning models. In addition, we focused on the problem of data availability and preparation for process mining, by adopting (i) techniques from image recognition like small sample learning, and (ii) clustering methods to pre-process event logs.
Process Execution Support for Distributed, Event-Driven IoT Environments: Innovative Smart Device Interfaces
We developed wearable user interfaces that allow humans to be notified in real-time at any location in case new tasks occur. In many situations humans must be able to directly influence data of IoT objects, e.g., to control industrial machinery or to manipulate certain device parameters from arbitrary places. We implemented an approach towards a framework for IoT data interaction by means of wearable process management. Additionally, humans can actively influence environmental data, e.g., production parameters, in real-time from arbitrary locations. This approach is based on speech recognition fueled by research in neural networks. We rely on an end-to-end (E2E) model approach that runs entirely on a smart device.
Methods and Models for Internet of Things-based Business Process Improvement (BPI)
Our latest line of research aims at providing methods, models, and guidelines that support organizations to effectively exploit the value propositions of IoT for improving business processes. The objective is therefore a holistic consideration of IoT-based BPI including major existing challenges that prevent organizations from performing beneficial IoT projects. The topic of IoT-based BPI encompasses and connects the research areas of IoT and Business Process Management (BPM) respectively BPI.
At first, the identification of problems and challenges, as well as the investigation of potential opportunities for specific processes has been conducted. With regard to IoT technology, it must be clearly elaborated to what extend IoT can be exploited for BPI and which value propositions, fitting the respective processes, can be anticipated by decision makers. This core principle can be denoted as identifying possible IoT-based BPI propositions. Second, after having identified potential value propositions, the decision on appropriate IoT-based BPIs is required. Organizations need to be supported in the selection phase for specific IoT technologies and applications that fit the anticipated BPI goals and the underlying process details. Also, organizations need to have detailed knowledge about their internal capabilities to successfully realize these projects. As these capabilities can change over time, a continuous maturity assessment has been developed. This principle can be summarized as investigating advanced methods that support the selection of IoT technologies and applications.