Project Details
Projekt
Conformance Checking with Regulations
Subject Area
Data Management, Data-Intensive Systems, Computer Science Methods in Business Informatics
Term
since 2021
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 465904964
Business processes often have to follow specific prescribed regulations, such as clinical practice guidelines in healthcare, laws and statutes in public administration, or the new hygiene rules in many different domains. For both organizational success and official audits, it is essential to know: Are we following the prescribed regulations? If we deviate, why? Should we improve employees’ training? Could the rules be adapted to be better applicable in the real world?A major benefit of process mining is that it provides insights into the real execution of business processes and techniques for evidence-based process analysis. Conformance checking, a main task of process mining, comprises techniques for checking the relation between a designed process model and the real-life behavior, and identify as well as analyze deviation between them. Hence, it allows organizations to answer the above-raised questions. However, to realize conformance checking on process regulations, major challenges need to be addressed. First, regulations are typically written as long and ambiguous texts, so they cannot be used directly for conformance checking. Reference models, i.e., process model templates that serve to be re-used for the design of other processes, can bridge this gap between regulations and process execution data. However, transforming regulations into reference models requires high manual effort, and research on automating this step is still at the beginning. Second, modern conformance checking techniques are computationally efficient, but unable to judge the deviation’s relevance, consider multiple models, or decipher desirable (positive) from undesirable (negative) deviations, all of which are important when dealing with regulations. In addition, there is only limited research on how to visualize conformance checking results, particularly if the intended audience is business users. Finally, process execution data is much more detailed than reference models, such that a high degree of automated abstraction is required to connect them.The CheR project combines, for the first time, techniques from reference modeling and conformance checking to compare real-life process behavior with prescribed regulations. The goal is to find and visualize the deviations between them to allow tailored training for employees, preparation of audits, or suggestions for improving either the process or the regulations in the respective domains. Several open aspects need to be targeted to allow conformance checking with reference models, including (1) supporting (semi)-automatic generation of reference models, (2) the extraction of useful event logs for this type of process mining project, (3) benchmarking of existing conformance checking methods and their possible extension, and (4) an empirical evaluation on how the CheR approach allows to leverage conformance checking with regulations, e.g., for training employees.
DFG Programme
Research Grants
Co-Investigator
Professor Dr. Ingo Weber
