Theses

To test and experiment with ideas and newly developed approaches in the BPM area, we need real-use case scenarios that illustrate challenges and allow the testing of developed techniques. This thesis focuses on documenting a business process, with the domain being freely chosen. In addition to creating a process model, the focus is on defining dependencies between activities and the contextual influence on the process. Furthermore, an extended event log will be generated, e.g., through simulation of the process model, containing attributes that capture the contextual information.
Objective/Task: Provide a use case where all activity dependencies are specified and the contextual influence is clearly defined. A process model should be designed, and an event log generated.

Traditional vendors rely on intermediate distributors to sell and ship their products to customers, storing their goods in warehouses and using traditional shipping routes In contrast to this, emerging online retailers (such as Temu or Aliexpress) allow shipping goods directly to customers, usually via airplane to shorten delivery times. However, one major risk is that customs withholds or rejects these goods, either due to faulty customs declarations or missing certificates. The major question to be addressed in this thesis is in how far the environmental impact of the two types of supply process differs.
Goal: Model the two supply process variants; analyse and compare their environmental impact using life cycle assessment (LCA) and the SOPA framework for sustainability-oriented process analysis. Make justified assumptions, where necessary

During process execution, upcoming process tasks must be allocated to internal resources, such as humans or machines. Allocating tasks to resources is a complex decision-making problem that has a high impact on the effectiveness and efficiency of business processes. Reinforcement learning is a promising concept to support the allocation of tasks in business processes. Multiple studies have been proposed in this field that show similarities and differences in modeling the states of the environment and the action of the agent.
Goal: Identify and analyze research works using reinforcement learning and identify similarities and differences. Propose a more general approach for reinforcement learning in the resource allocation for business processes and define configuration possibilities.ign and automation of business processes.

Understanding the contextual rationale behind activity relationships in business process models is crucial for preventive business process redesign. Based on the motivational origin of an activity relationship, we can derive risks and consequences when changing it. Previous approaches have explored the extraction of explanatory rationales using Large Language Models (LLMs) and questionnaires. However, these methods rely on post-hoc analysis rather than integrating rationale elicitation into the modeling process itself. This thesis topic aims to develop an interactive approach that captures the rationale for activity relationships during business process modeling. Specifically, when a relationship is added, the modeler will be prompted to specify whether it is driven by legal requirements, business rules, or best practices. To achieve this, a user interface will be implemented, for example, based on BPMN-js, seamlessly integrating rationale elicitation into the modeling workflow. 

Objective/Task: Develop a prototypical UI so that users have to provide an explanatory rationale for each relationship they are modeling. The entered data should be stored accordingly.

Deciding which (human) resource to assign to a task is an important decision, as resources on one side are limited and on the other side have diverse individual behavior, influencing the performance and outcome of the process instances. Resource Allocation Patterns are used to describe common mechanisms to perform this decision. However, they are defined on a rather informal level and are thus unsuitable for automated reasoning on resource allocation knowledge graphs.

Task: Investigate how resource allocation patterns (see source below) can be expressed as formal graph rules. Analyze the suitability of each rule (category), formalize the rule, and implement this formalization.

http://www.workflowpatterns.com/documentation/documents/Resource%20Patterns%20BETA%20TR.pdf

Business Process Simulation (BPS) supports process analysis and decision-making by modeling and evaluating different process scenarios. While automation plays a key role in increasing efficiency, human involvement remains essential at various stages, including data preprocessing, conceptual model development, validation, execution, and results interpretation. This thesis aims to investigate the specific roles of human input across one or two of these stages and to develop a framework for achieving an optimal balance between human expertise and automation. The goal is to enhance the accuracy, reliability, and practical relevance of simulation outcomes by systematically integrating human insights where they add the most value.

This research intends to examine the current state of data and analytics architecture within the context of Enterprise Architecture Management (EAM). As organizations increasingly adopt data-driven decision-making, integrating data and analytics into EAM frameworks is crucial for ensuring strategic alignment, scalability, and governance. This study aims to identify and analyze existing literature to determine the current state in managing data and analytics architecture within EAM. By conducting a systematic literature review the research should provide insights into how organizations can optimize their enterprise architecture to better support data-driven strategies. The findings will contribute to a deeper understanding of the current state and summarize data and analytics capabilities within EAM frameworks.
Goal: Conduct a literature review to explore the current state of data & analytics architecture within EAM. Identify and summarize the key findings of the literature reviews and give recommendations.

Existing frameworks such as SOPA allow process experts to holistically analyse and improve the environmental impact of business processes, drawing on techniques such as business process simulation and process engines for specifying impact during process execution. However, the two analytical perspectives, being execution and simulation, are so far treated separately. Goal: To bridge this gap, this topic aims to extend an existing tool for extracting business process simulation scenarios from process execution data, so that sustainability information (i.e., environmental cost drivers) entered during process execution can be re-used during process improvement and simulation. Further, the underlying SOPA framework should be formally extended if needed. A resulting implementation should be demonstrated in a realistic use case scenario.

Due to the constantly changing environment, business processes must adapt their process behavior. This means that relations between the activities of a process are changed to make it more efficient with regard to cost, time, and quality. Business process simulation helps in testing new process scenarios safely in a computer-imitated environment and can measure the implications on performance, quality, and costs. However, not every relationship between activities can be changed. Some of them are essential for proper process termination and has to be ensured. Other relationships can be changed but are associated with certain risks and consequences. This thesis is therefore concerned with how business process simulation can be further developed for process behavior redesign so that the vulnerability of relations is taken into account. Specifically, it will be investigated to what extent machine learning approaches, in particular, can be used to develop new process scenarios that take into account the vulnerability of activity relation learnt from historical data and provided by human input.
Goal: Development of a machine learning approach which identifys and tests new process scenarios with a process simulation changing the process behavior by taking into account the vulnerability of activities.

Conformance checking is a fundamental technique in process mining, used to assess whether a business process model accurately reflects real-world process execution recorded in event logs. Traditionally, conformance checking compares a process model against execution traces to identify deviations. This research introduces an alternative approach by leveraging activity relationship types instead of event logs to evaluate conformance. The approach is based on the activity relationships matrix, which defines relationships between activity pairs using two types of dependencies: temporal dependency and existential dependency. This allows for a structured representation of how activities relate to a process. The goal of this research is to develop an algorithmic method to compare a given process model with the activity relationships matrix and determine to what extent the discovered relationships conform to the predefined matrix. A prototype implementation will be developed to validate the approach, providing insights for identifying deviations based on relationships. This research aims to enhance the precision and flexibility of conformance checking by shifting the focus from event-driven evaluation to structural relationship validation.

Knowledge is essential for the execution of business processes as driving factor for deciding how the process should continue and for anticipating events. Encoding this knowledge in a knowledge graph is the first step to utilize it for intelligent process support systems. However, getting the knowledge into the graph is hard task, so far only performed for other domains.

Task: Investigate existing knowledge graph extraction techniques for creating a process knowledge graph from textual descriptions. Formulate requirements for such approaches, inventorize and adapt appropriate techniques, and evaluate them using an example process.

Deciding which (human) resource to assign to a task is an important decision, as resources on one side are limited and on the other side have diverse individual behavior, influencing the performance and outcome of the process instances. A wide range of knowledge thus must be considered to make optimal decisions, such as availabilities and aptitudes, but also the expected process continuation. A simple approach has already been developed to perform rule-based reasoning on allocation knowledge graphs. However, this existing approach only considers locally best decisions and is thus limited w.r.t. planning.

Task: Design, implement, and evaluate an approach to perform forward-looking, rule-based resource planning on resource allocation knowledge graphs.