Description

This master thesis aims to apply process mining techniques to analyze the microservice architecture underlying forecasting software, focusing on uncovering insights into the execution behavior of forecasting tasks. The thesis will examine the microservice architecture in-depth, identifying key components, interactions, and data flows involved in the forecasting process. Process mining methodologies such as process discovery, conformance checking, and performance analysis will then be employed to gain insights into the end-to-end process flows, including data ingestion, preprocessing, model training, evaluation, and deployment.