Theses

The enactment of business processes can have a negative impact on the environment, meaning that environmental impacts of business processes need to be analysed. One existing framework, the SOPA framework, uses lifecycle assessments (LCA) to incorporate this impact. However, the analyses happen on a level that abstracts from real-world process executions. During run-time, process engines used to execute processes can be used to a) track run-time data and b) require user input on certain activities so that the environmental impact analyses can be made more exact. Relevant for this is an understanding of the base units used in LCA, which can be used for refinement of the analyses.
Goal: Extend a business process engine so that additional data relevant for environmental impact analyses can be put in/tracked during execution time. Evaluate this with an example.

Event logs capture the actual workflows within a company, identifying deviations, recognizing bottlenecks, and pinpointing inefficient activities. As an objective data source, they are often used for evaluating proposed approaches and ideas within the community. They enable scientists and practitioners to empirically study business processes, develop new models, and suggest improvements. However, a problem arises as event logs are often distributed across various websites and formats, lacking a central overview. Finding the appropriate dataset for a specific analysis or evaluation in business process management is time-consuming and challenging.
Objective/Task: Compile an overview of available data in the BPM area and explain which datasets suit the different application areas. Find an appropriate grouping that maps process datasets to BPM-related research questions.

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. The allocation of tasks to resources is a complex decision-making problem with a high impact on the effectiveness and efficiency of business processes. A wide range of system-initiated (primarily automated) resource allocation approaches have been developed over the last decades. We have analyzed them in a structured literature review. In this, we have learned that benchmarking these approaches is still challenging. To prepare a benchmark, the identified studies of this literature review need to be further coded regarding different aspects, such as allocation goal, control-flow constraints (considered yes, no, assumptions), constraints regarding the resources, constraints regarding tasks, allocation capability, run-time/preplanning, etc.
Goal: Develop a data set and a search interface to identify resource allocation studies for a benchmark.
 

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.

Organizations carry out a variety of business processes to meet the needs of their customers. These processes, which consist of coordinated activities designed to accomplish specific objectives, are collectively known as business processes. Business Process Simulation (BPS) plays a crucial role in this context by providing a powerful tool to create virtual environments for testing process modifications without actual real-world consequences. Machine learning is one of the recent technologies that are used to enhance and optimize business process simulation. Goal: to provide an overview of recent efforts in integrating machine learning technologies to enhance business process simulation.

Business process engines that are augmented by AI promise highly autonomous process orchestration. However, human process participants are still integral to the processes executed. Consequently, the human-machine interface must include functionalities for the system to proactively contact human actors, e.g., to ask for recontextualization, to escalate decisions in unknown situations, or to create awareness of abnormal situations.
Goal: Identify requirements for the human interface of intelligent process automation systems. Conceptualize how these requirements can be implemented and create a prototype.

Business process simulation is a key method for predictive process improvement. The more precise a simulation model reflects (potential) reality, the more valuable insights can be derived from it. To this end, information from multiple sources, such as process mining tools, databases, and human knowledge must be considered. This presents a challenge to simulation experts, who then need to manually integrate different tools and their data formats. Visual models promise to make simulation model discovery pipelines tangible and automatable, addressing this challenge.
Goal: Develop a user-friendly mechanism to generalize the integration of different tools for the discovery and parameterization of business process simulation models. Evaluate the developed artifact.
 

Process models are very useful artifacts for describing the control flow of activities and the execution sequence. However, domain knowledge that also encompasses the backgrounds of relations and the motivation behind why activities are part of the process is usually not represented. This knowledge remains with the process participants. One technique to access this knowledge is through questionnaires. Here, process participants must participate in a kind of survey that systematically queries knowledge. Ideally, the responses can then be evaluated automatically. This thesis aims to apply this technique to a use case scenario. The specific use case can be freely chosen.
Goal: Apply the questionnaire technique to a use case.

When analysing the environmental impact of business processes, existing works assign discrete environmental costs to activity instances of that processes, and thereby arrive at the overall assessment of environmental process costs. However, determining the costs of activity instances is very complex and difficult to confirm, and therefore connected with a degree of uncertainty. Some techniques for quantifying environmental impacts, such as life cycle assessment (LCA) explicitly include this uncertainty in their results. This information on uncertainty could be useful in enriching existing approaches with details on how certain the assessment of environmental process costs is.
Goal: Develop an approach that takes information on uncertainty into account when calculating environmental process costs, and integrate this into the existing implementation of the SOPA framework for sustainability-oriented process analysis.

Business process management assumes a clearly defined start and end of business processes. However, not all business processes adhere to this logic, and as a consequence, BPM analysis techniques cannot appropriately capture their behavior. In previous work, we introduced the notion of vitalizing business processes that target the lifecycle process of one or more entities. The MedEvaluate the Concept of Vitalizing Business Processes on the MIMIC-IV data setical Information Mart for Intensive Care (MIMIC)-IV database is a publicly available relational database that includes data on patient treatment in a tertiary academic medical center in Boston, USA. It provides complex care processes in a hospital from end to end. It includes many of the vitalizing business processes.
Goal: Apply the notation of vitalizing business processes and the proposed concepts to one patient group of the MIMIC-IV data set. Evaluate the completeness and usefulness of the proposed concepts.

Organizations execute diverse business processes to serve their customers. Business processes can be defined as a series of structured, coordinated activities aimed at achieving goals (e.g., hiring a new employee). Business Process Simulation (BPS) steps in here – a potent tool creating a virtual environment for testing process changes without real-world effects. The quality of input data is crucial for the effectiveness of business process simulation, particularly event logs, which capture real-life process executions. However, real-world event logs often suffer from various quality issues, affecting simulations' accuracy and reliability.
Goal: aims to provide a comprehensive overview of data quality challenges, specifically focusing on issues that directly impact simulation outcomes. then conducting extensive practical testing on available datasets to analyze their suitability and quality for simulation purposes.

Changes are omnipresent in todays world. Regulations and laws change as well as IT technologies. In order to keep pace with these changes, business processes must also be constantly modified. Business process redesign is the sub-discipline of business process management (BPM) that deals with changing business processes with the aim of making the process more efficient or implementing changes in regulations or technologies at the process level. Process models, especially BPMN process models, are used in particular for changes to process behavior, i.e. changing activity relationships. BPMN is a modeling language that is particularly suitable for static and highly structured processes. Modeling languages such as DECLARE or DCR notations are used for modeling more knowledge-intensive and flexible processes. However, every process model abstracts from the activity relationships that exist in the model making business process redesign challenging for the user. Because so far, changes are mostly done manually. The question now is to what extent the various modeling approaches are suitable for carrying out behavioral business process redesign. Specifically, this thesis will compare modeling approaches and test the manual implementation of concrete change operations on different models in order to have an overview of the strengths and weaknesses of the approaches in context of business process redesign.
Goal: Provide an overview of strengths and weaknesses of modeling approaches for a set of change operations that are performed on process models created with these notations. In particular, compare BPMN, DECLARE, DCR, CMMN, and a new approach focusing on temporal and existential dependencies.

Clinical studies are well documented in central storage, especially in the USA (https://clinicaltrials.gov/data-api/about-api). This availability of public data on clinical studies allows process mining to be applied to this data to find new insights or to evaluate new process mining techniques on this data set. However, in preparation, the data must first be transferred into an event log. Therefore, decisions on the case notion, the selected event types, and their attributes have to be made. Experts in this field can support the process of extracting an event log.
Goal: Follow the procedure of building a useful event log by Jans et al. and extract an event log with the help of experts with whom we are in contact for applying process mining techniques on the publicly available clinical study data.

Knowledge is a comprehensive term but essential for various research domains. Also, in Business Process Management (BPM), process knowledge is crucial for understanding processes and dependencies between individual activities. Knowledge includes more than just execution sequences and data encoded in event logs. To make the concept of process knowledge more tangible, the theory of knowledge and knowledge management should therefore be examined more closely in this thesis. What is process knowledge? How can we classify this kind of knowledge? Are there different types of knowledge that are represented and encoded differently? How can we extract knowledge from data and use this knowledge, for example, for process redesign?
Goal: Conduct a literature review on the theory of knowledge and relate the concepts with process knowledge to support the redesign and automation of business processes.

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.