In the field of process analysis, process mining has made it possible to discover hidden patterns in event logs. However, one of the most significant challenges arises when working with Local Process Models (LPMs): model explosion and structural repetition. When algorithms generate hundreds or thousands of LPMs, analysts cannot examine them manually, and selecting the best ones by score does not always provide a representative view of the actual process. This problem, recently identified in academic research, underscores the need for a smarter approach to grouping and selecting optimal samples of LPMs, based not only on similarity metrics between models but also on the context of data attributes in the event log.
From a business perspective, correctly understanding local patterns allows for optimizing workflows, detecting bottlenecks, and improving decision-making. To achieve this, companies can rely on custom applications and custom software that integrate process mining techniques with artificial intelligence. At Q2BSTUDIO, we develop solutions that combine AI for businesses with business intelligence service tools like Power BI, enabling analysts to visualize and filter large volumes of LPMs without saturation. Additionally, the implementation of AI agents can automate model grouping based on contextual similarity, reducing repetition and improving the coverage of meaningful patterns.
The proposal of a grouping framework for LPMs not only solves a technical problem but also opens the door to more efficient process automation. By integrating process automation solutions with cloud services such as AWS and Azure cloud services, organizations can scale event analysis without losing granularity. Cybersecurity also plays a crucial role in protecting event logs during massive data processing, ensuring that sensitive information is not exposed. Finally, the combination of artificial intelligence for businesses with local model grouping techniques allows moving from a biased sample to a faithful representation of operational reality, maximizing the value of process mining in any industry.
