# Predictive Business Process Monitoring Approach Based on Hierarchical Transformer
## 1 Introduction  
Process mining, as a cross-field of research in business process management fields, data mining, and formal modeling and analysis, aims to discover process knowledge from the massive event logs of modern information systems, ensuring monitoring and improvement of real business systems [1]. Process mining has recently received much attention from academia and industry and is recognized as one of the key technologies for achieving new business intelligence. Predictive business process monitoring is an emerging research direction in process mining, where the goal is to predict the possible next state of an executing business process instance. The common predictive goals are the next executed activity of the business process, the remaining execution time, the execution result, etc. Accurate business process prediction results enable effective predictive monitoring of business processes, which is valuable in tasks such as optimizing business process resource allocation, execution bottleneck analysis, efficiency optimization, and avoiding unintended execution outcomes [2]. As organizations such as enterprises become more complex, systems, applications, and devices in the organization generate a large number of logs and sensor data, so process mining requires the automation of analysis and the extraction of valid information from the massive amount of data. Moreover, the interaction between different business processes is very complex, so it is important to discover a comprehensive view of the entire business process by virtue of its comprehensive ability to analyze multiple business processes simultaneously. Finally, as processes change with technological advances or resourcescheduling, previous process models no longer match current processes, and process mining requires analyzing them in real-time, making process models more sensitive to data changes. Early research in predictive business process monitoring focused on constructing formal models of business processes such as Transition Systems [1], Finite State Machine [3], and Stochastic Petri Nets [4] to predict the subsequent state of business process instances. With the growing development of information technology in various enterprises and institutions in recent years, more and more business process execution data have been recorded as event logs, greatly facilitating data-driven business process prediction research. Researchers have attempted to apply machine learning methods such as Hidden Markov Models [5], Support Vector Machines [6], and Cluster Analysis [7] to the task of business process prediction. For example, automobile insurance claims contain parallel execution of tasks, mapping them to an extended space Markov chain can make predictions. Support Vector Machines classify input object features relevant for the classification on the basis of its position in hyperspace and then check possible anomalies in its behavior. Cluster Analysis is applied to previous traces of the patient, and then a classifier is built for each cluster to discriminate whether or not a cancer patient. Machine learning methods can overcome the shortcomings of earlier formal modeling methods that could not adequately reflect the complex business process execution logic in real business systems and achieve better prediction results. However, the application of machine learning methods for business process prediction requires tedious feature engineering, and different business processes are often variable. It leads to no fixed pattern to follow in extracting business process features. In addition, traditional machine learning models have a more limited parameter space, making it difficult to effectively model large volumes of event logs. To address this problem, researchers start applying deep learning methods to business process prediction tasks in recent years, and the prediction results have further improved.