An Interactive Digital Twin for Visual Querying and Process Mining

Large volumes of structured, semi-structured and unstructured data are produced daily by industrial businesses which require analysis and processing with appropriate models and algorithms to obtain valuable knowledge. This paper introduces a framework for business technology that combines the notion of Digital Twins with Process Mining aiming at delivering a simple and efficient way to retrieve customized data and process it with the use of graphical techniques, providing interactive visualization of process mining steps. More specifically, the proposed framework provides the ability to define different data sources and link these sources with a visual query generator which constructs, executes and depicts graphically the results of custom queries. The framework includes also sophisticated Artificial Intelligence / Machine Learning algorithms for data analysis, filtering and prediction. The framework is demonstrated through an interactive dashboard, which was implemented in Python to support a fully operational and visual process mining environment that facilitates decision making witho