PM4Py is a Python library providing a comprehensive array of tools for process mining. This paper presents an in-depth overview of the PM4Py library, including its integration with other Python libraries and its latest features, such as object-centric process mining. Furthermore, we discuss the significant impact of PM4Py within academia, industry, and the open-source community, evidenced by its wide adoption and substantial evolution. In short, the PM4Py library is an essential tool for researchers and practitioners, paving the way for advancements in process mining. 

PM4Py: A process mining library for Python

Process mining techniques help to improve processes using event data. Such data are widely available in information systems. However, they often contain highly sensitive information. For example, healthcare information systems record event data that can be utilized by process mining techniques to improve the treatment process, reduce patient’s waiting times, improve resource productivity, etc. However, the recorded event data include highly sensitive information related to treatment activities. Responsible process mining should provide insights about the underlying processes, yet, at the same time, it should not reveal sensitive information. In this paper, we discuss the challenges regarding directly applying existing well-known group-based privacy preservation techniques, e.g., k -anonymity, l -diversity, etc, to event data. We provide formal definitions of attack models and introduce an effective group-based privacy preservation technique for process mining. Our technique covers the main perspectives of process mining including control-flow, time, case, and organizational perspectives. The proposed technique provides interpretable and adjustable parameters to handle different privacy aspects. We employ real-life event data and evaluate both data utility and result utility to show the effectiveness of the privacy preservation technique. We also compare this approach with other group-based approaches for privacy-preserving event data publishing.

Group-based privacy preservation techniques for process mining

Many organizations have started digital transformation initiatives. New digital tools are available with increasing regularity – and many of them have a major impact on business processes. However, only a small number of organizations have their business processes sufficiently under control to realize the full business potential of new digital technologies. Appropriate business process management (BPM) capabilities, have a significant impact on the value achieved through digitalization. This is especially true for establishing appropriate business process governance. Process governance keeps processes on track. It identifies necessary adjustments of the process, defines the required actions, and ensures their execution. This has a significant impact on the realization and sustainment of the targeted digitalization benefits as well as the ongoing performance of the processes. However, digital operational processes have new requirements for process governance. And digital process management tools provide new opportunities for effective governance. Therefore, process governance must go through a digital transformation itself, leveraging appropriate tools such as process mining or dynamic process modelling and simulation tools. Result is digital process governance, an important foundation of successful digital transformation.

Digital Transformation of Business Process Governance

Prescriptive process monitoring based on causal effect estimation

In this paper, we propose a deep learning-based approach to predict the next event in hospital organizational process models following the guidance of predictive process mining. This method provides value for the planning and allocating of resources since each trace linked to a case shows the consecutive execution of events in a healthcare process. The predictive model is based on a long short-term memory (LSTM) neural network that achieves high accuracy in the training and testing stages. In addition, a framework to implement the LSTM neural network is proposed, comprising stages from the preprocessing of the raw data to selecting the best LSTM model. The effectiveness of the prediction method is evaluated through four real-life event logs that contain historical information on the execution of the processes of patient transfer orders between hospitals, sepsis care cases, billing of medical services, and patient care management. In the test stage, the LSTM model reached values of 0.98, 0.91, 0.85, and 0.81 in the accuracy metric, and in the evaluation of the prediction of the next event using the 10-fold cross-validation technique, values of 0.94, 0.88, 0.84, and 0.81 were obtained for the four previously mentioned event logs. In addition, the performance of the LSTM prediction model was evaluated with the precision, recall, F1-score, and area under the receiver operating characteristic (ROC) curve (AUC) metrics, obtaining high scores very close to 1. The experimental results suggest that the proposed method achieves acceptable measures in predicting the next event regardless of whether an input event or a set of input events is used.

A Deep Learning Approach for Predictive Healthcare Process Monitoring

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Business Process Management - 18th International Conference, BPM 2020, Seville, Spain, September 13-18, 2020, Proceedings

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Business Process Management - 18th International Conference, BPM 2020, Seville, Spain, September 13-18, 2020, Proceedings