Conceptualizing Business Process Violations - Dunzer, Sebastian; Ließmann, Annina; Stierle, Matthias; Matzner, Martin (CEUR Workshop Proceedings, 2024)
Process analysts seek to gain insights into process execution to continuously improve their business processes. Business process deviation analysis can prove useful in identifying process improvements. However, differentiating between relevant and irrelevant deviations poses challenges. This is due to commercial process mining software flagging a high number of process instances as deviations from process definitions (e.g., process models). Against this background, we define and conceptualize business process violations that indicate undesired behavior. This paper defines business process violations based on regulatory business process compliance. Furthermore, this paper conveys a conceptual model connecting business process violations, the process definition they violate, and their organizational setting. We structure these components in ten dimensions of business process violations. The conceptual model and dimensions were developed and evaluated based on real-world examples provided by a German software vendor. Our conceptualization provides the vocabulary and raises awareness to discuss deviations’ important aspects that delineate violations
Keywords: business process deviations; business process violation; process analysis; conformance; compliance

Event Log Construction from Multimodel Data - A Reference Architecture for Exploiting Process Mining in IT Service Management - Reinhard, Philipp; Ließmann, Annina; Weinzierl, Sven; Zilker, Sandra; Li, Mahei M.; Matzner, Martin; Leimeister, Jan Marco (Proceedings of the 33rd European Conference on Information Systems, 2025)
Process mining holds substantial potential to discover and optimize processes utilizing event log data. However, current applications primarily rely on (semi-)structured data from process-aware information systems, limiting their capacity to incorporate multimodal data from diverse sources, particularly in domains like IT service management (ITSM). While existing stand-alone approaches can extract event log data from unstructured sources such as videos, documents, or bot logs, they fall short of leveraging the full range of real-world data available in ITSM. To address this gap, our research focuses on developing a reference architecture for constructing event logs from multimodal data. This architecture integrates diverse data types, construction functions, and process mining use cases. Following a design science research methodology, we aim to evaluate the architecture through a software artifact leveraging real-world ITSM data and incorporating state-of-the-art generative AI. In this study, we present the preliminary reference architecture and share early insights from expert evaluations.<br

Predicting Customer Satisfaction in Service Processes Using Multilingual Large Language Models - Ließmann, Annina; Zilker, Sandra; Weinzierl, Sven; Sukhareva, Maria; Matzner, Martin (Proceedings of the 57th Hawaii International Conference on System Sciences, 2024)
The huge amount of data recorded during business process executions in today’s organizations creates the need to leverage this data. While most existing business process monitoring methods are capable of including structured context information, the incorporation of unstructured information, for example, text, has rarely been researched. Recent advances in natural language processing offer tools to generate contextualized sentence embeddings, which capture more information in human language than ever before. Especially in service processes, such as the incident management process, a variety of unstructured information is created throughout the execution, representing a relevant use case for incorporating (multilingual) text. To close this research gap, a method exploiting multilingual text for predicting the outcome of a service process is presented. Multilingual large language models are used to generate sentence embeddings for unstructured text created during process execution. After instantiating the method, an evaluation was performed using a real-life event log to predict customer satisfaction in the incident management process at a German multinational corporation. We show that text incorporation improves predictive performanc
Keywords: predictive process monitoring; natural language processing; machine learning; service analytics; customer satisfaction

Recent Advances in Data-Driven Business Process Management - Ackermann, Lars; Käppel, Martin; Marcus, Laura; Moder, Linda; Dunzer, Sebastian; Hornsteiner, Markus; Ließmann, Annina; Zisgen, Yorck; Empl, Philip; Herm, Lukas-Valentin; Neis, Nicolas; Neuberger, Julian; et al. (2024)
The rapid development of cutting-edge technologies, the increasing volume of data and also the availability and processability of new types of data sources has led to a paradigm shift in data-based management and decision-making. Since business processes are at the core of organizational work, these developments heavily impact business process management (BPM) as a crucial success factor for organizations. In view of this emerging potential, data-driven business process management has become a relevant and vibrant research area. Given the complexity and interdisciplinarity of the research field, this position paper therefore presents research insights regarding data-driven BP
Keywords: BPM; Process Mining; Research Agenda; Data-centric

Transfer Learning for Predictive Process Monitoring - Ließmann, Annina; Wang, Weixin; Weinzierl, Sven; Zilker, Sandra; Matzner, Martin (Proceedings of the 32nd European Conference on Information Systems, 2024)
Event log data reflects the behavior of business processes that are mapped in organizational information systems. Predictive process monitoring (PPM) transforms this data into value; it creates process-related predictions to provide insights required for proactive interventions at process runtime. Existing PPM techniques require sufficient amounts of event data or other relevant resources that might not be readily available, preventing some organizations from taking advantage of PPM. In this paper, we present a transfer-learning-based technique for PM, allowing organizations without suitable data or other relevant resources to implement PPM for effective decision support. We instantiate our artifact, and apply and evaluate it on a real-life use case. The use case includes two event logs for purchase-to-pay processes of two organizations. Our results provide evidence that knowledge of a process of one organization can be transferred to a similar process of another organization to enable PPM in the target organizatio
Keywords: predictive process monitoring; process mining; transfer learning; language model