In the DIfA project, the use of anomaly detection using autoencoders was evaluated in the context of audit tests. The results were published in a paper titled “Using Autoencoders for Data-Driven Analysis in Internal Auditing”, which was accepted at the Hawaii International Conference on System Sciences (HICSS) 2021 and will be presented and published from January 04 to 09. HICSS 2021 will be held virtually and participation is free of charge for visitors (https://hicss.hawaii.edu/)
In mid-September 2020, two master’s theses started, whose topics are embedded in the DIfA project. Analogous to the structure of the DIfA project, the focus of these master theses is on the areas of unsupervised anomaly detection and natural language processing. The two master students are supervised by the project leaders Jakob Nonnenmacher and Gerrit Schumann.
This study investigated how digital traces left behind during the execution of business processes can be examined for anomalies using process mining and unsupervised machine learning methods. The findings of the study were used to write a paper that was accepted at the International Conference for Service Oriented Computing (ICSOC 2020). The paper will be presented at a virtual conference from December 14th to 17th and subsequently published.
On June 9, 2020, the first project half-year meeting with the project managers, the steering committee and the project leaders took place. At the meeting, the goals achieved so far, current activities and, in essence, the agreed definition of goals were discussed. Due to the corona situation, the project meeting was conducted virtually.
On Friday, December 13, 2019, the kick-off meeting for the DIfA project took place in the premises of the Department of Business Informatics / VLBA of the University of Oldenburg. Among other things, the meeting focused on the organizational and project structure as well as the concretization of the cooperation and external communication.