Declare MGS: Model Generator and Specializer

Recent focus in process mining has revolved around assessing algorithms like process discovery and conformance checking. To properly evaluate these algorithms, having a reference process model is essential. This model is used for comparing algorithmic results or creating synthetic event logs, making synthetic models ideal for systematic algorithm evaluations. In experimental settings, researchers want to assess algorithms on different levels of process behavior. Therefore, it is required that synthetic models can be adjusted in terms of behavior. In this paper, we propose techniques to generate and specialize declarative process models and evaluate their performance. Evaluation results reveal that the requested number of constraints negatively affects the success of model generation and positively affects the execution time. In contrast to the given number of activities, which positively affects success and negatively affects execution time. The number of detected inconsistencies and redundancies during model generation seem to be mechanisms that contribute to less success and longer execution times.

Hi! I'm Manal, a passionate fifth-year PhD researcher delving into the intriguing world of AI in financial auditing. Imagine the challenge auditors face when sifting through thousands of transactions to ensure the reliability of a company’s financial statements. In our tech-driven era, my research explores a game-changing approach – stepping away from traditional sampling and embracing automated testing for all financial transactions. But here's the catch: the automated approach floods auditors with alarms, while the auditor is genuinely interested in, let's say, 5% of them, as they might impact the financial statements. My project’s mission? Untangle this alarm overload by crafting an efficient solution for investigating each alarm and distinguishing true alarms from acceptable exceptions. I'm developing an interactive machine learning technique, paving the way for auditors to focus their valuable time on alarms that do really matter.