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Proactive monitoring of business processes has become a key competitive advantage for firms, enabling timely interventions to prevent workflow deviations. Process-aware information systems generate extensive logs, which serve as valuable resources for predictive analytics. In this context, this study presents a deep learning-based approach for predicting the next event in an ongoing process by analyzing historical execution logs. The proposed method formulates event prediction as a classification task, leveraging n-gram encoding and feature hashing for effective feature preprocessing. The model consists of a multi-stage deep learning framework, incorporating stacked autoencoders for unsupervised pretraining, followed by a supervised fine-tuning phase to optimize classification accuracy. Experimental validation was conducted on six real-life event log datasets, including BPI Challenge 2012 (subsets A, O, W), BPI Challenge 2013 (Incidents, Problems), and Helpdesk logs. The proposed approach achieved up to 83.1% accuracy, 85.2% precision, and 92.3% AUC on the BPI 2012_A dataset, outperforming stateof-the-art classifiers such as LSTM-based models and Bayesian regularized PFAs. Notably, it demonstrated a 6–11% improvement in recall over existing methods on key datasets. The results highlight the model’s ability to capture complex process dynamics and improve proactive situation awareness. Additionally, the study explores the impact of hyperparameter tuning and addresses data imbalance challenges using RBF-based synthetic data generation, contributing a robust framework for real-time decision support in business process management.

Process mining enables the reconstruction and evaluation of business processes based on digital traces in IT systems. An increasingly important technique in this context is process prediction. Given a sequence of events of an ongoing trace, process prediction allows forecasting upcoming events or performance measurements. In recent years, multiple process prediction approaches have been proposed, applying different data processing schemes and prediction algorithms. This study focuses on deep learning algorithms since they seem to outperform their machine learning alternatives consistently. Whilst having a common learning algorithm, they use different data preprocessing techniques, implement a variety of network topologies and focus on various goals such as outcome prediction, time prediction or control-flow prediction. Additionally, the set of log-data, evaluation metrics and baselines used by the authors diverge, making the results hard to compare. This paper attempts to synthesise the advantages and disadvantages of the procedural decisions in these approaches by conducting a systematic literature review.

The process of cement manufacturing is both energy intensive and difficult to control. This complicated process results in inefficiencies in energy consumption and variations in cement quality with many complex influencing process factors such as input raw materials, variable fuels, firing conditions including temperature, burning, and reside time. Therefore, in order to address these challenges and investigate the effect of parameters and system optimization, the processes must be modeled first. This predictive model will be used to support process energy use reductions while maintaining and improving product quality. This article presents a study on the use of machine learning models to predict clinker kiln flow rate based on process parameters. The study tested different models such as linear regression, Extra Trees regressor, random forest, K-nearest neighbor, XGB regressor, and neural network and found that the linear regression model performed the best due to its ability to handle overfitting pretty well using dimensionally reduction techniques, regularization, and cross-validation. In fact, the predictive model found enable to predict kiln feed rate at an early stage based on a total of 91 significant input parameters and enable to make future suggestions for action to optimize the control of the kiln process. The findings have significant implications for the process and operation related to the kiln performances which implies potential reduction in terms of energy consumption and gas emissions and improvement of operational efficiency.

The ability to proactively monitor business processes is a main competitive differentiator for firms. Process execution logs generated by process aware information systems help to make process specific predictions for enabling a proactive situational awareness. The goal of the proposed approach is to predict the next process event from the completed activities of the running process instance, based on the execution log data from previously completed process instances. By predicting process events, companies can initiate timely interventions to address undesired deviations from the desired workflow. The paper proposes a multi-stage deep learning approach that formulates the next event prediction problem as a classification problem. Following a feature pre-processing stage with n-grams and feature hashing, a deep learning model consisting of an unsupervised pre-training component with stacked autoencoders and a supervised fine-tuning component is applied. Experiments on a variety of business process log datasets show that the multi-stage deep learning approach provides promising results. The study also compared the results to existing deep recurrent neural networks and conventional classification approaches. Furthermore, the paper addresses the identification of suitable hyperparameters for the proposed approach, and the handling of the imbalanced nature of business process event datasets.

The Lanbazi landslide, a typical reservoir landslide in the Three Gorges Reservoir, has exhibited significant and increasing deformation over the past two years, posing a severe threat to the safety of nearby residents’ lives and property. This study employed a combination of field investigation, engineering geological survey, SBAS-InSAR interpretation, and RAMMS numerical simulation to predict and analyze the spatial and temporal evolution of landslide deformation and the instability movement of the Lanbazi landslide. The results suggest that the deformation rate of the landslide ranges from − 73.5 mm/a to 24.7 mm/a from January 2022 to December 2024, and the deformation of the middle and rear edge of the landslide is the largest and the movement rate is the most significant. The RAMMS software is used to calculate the movement process of the secondary potential landslide instability area. The total time from the start to the end of the landslide is 275 s, the maximum movement speed is 25.2 m/s, the maximum movement accumulation height is 31 m, the maximum impact force is 1265.2 kPa, and the landslide accumulation body will eventually flow into the Yangtze River, which will produce a surge of up to 11.7 m. This study innovatively combines SBAS-InSAR and RAMMS numerical simulation technology to realize the collaborative analysis of landslide deformation monitoring and instability motion prediction. This method breaks through the separation problem of deformation analysis and disaster prediction in traditional research.

Proactive analysis of patient pathways helps healthcare providers anticipate treatment-related risks, identify outcomes, and allocate resources. Machine learning (ML) can leverage a patient’s complete health history to make informed decisions about future events. However, previous work has mostly relied on so-called black-box models, which are unintelligible to humans, making it difficult for clinicians to apply such models. Our work introduces PatWay-Net, an ML framework designed for interpretable predictions of admission to the intensive care unit (ICU) for patients with symptoms of sepsis. We propose a novel type of recurrent neural network and combine it with multi-layer perceptrons to process the patient pathways and produce predictive yet interpretable results. We demonstrate its utility through a comprehensive dashboard that visualizes patient health trajectories, predictive outcomes, and associated risks. Our evaluation includes both predictive performance – where PatWay-Net outperforms standard models such as decision trees, random forests, and gradient-boosted decision trees – and clinical utility, validated through structured interviews with clinicians. By providing improved predictive accuracy along with interpretable and actionable insights, PatWay-Net serves as a valuable tool for healthcare decision support in the critical case of patients with symptoms of sepsis.

Occupational health and safety has top priority within the cement industry. The preheating tower with its series of installed cyclones is essential in the cement kiln production process and it is considered among the most dangerous places in a cement plant. Coatings and blockages can often occur in the cyclone preheaters of rotary kiln plants for burning cement clinker. These wall build-ups disturb and/or block the process downward flow of hot kiln feed and the upward flow of hot kiln exhaust gases. Actually, our research aims to use process prediction by operating the digital transformation through a 4.0 tool for monitoring and analyzing temperature and pressure in real time. This tool monitors temperature and pressure using sensors that transform the data into a computer platform for real-time analysis and predicts failures according to a predictive model to prevent the occurrence of preheater cyclone blockages. This new technology will help to further improve occupational safety, increases the efficiency of industrial processes, and increases productivity.

Top-bottom combined blowing converter steelmaking involves complex thermodynamic and kinetic processes, and predictive modeling has long been a key focus in steelmaking research. This paper proposes a kinetic process prediction model with on-site applicability. Based on actual production data, machine learning models (BP neural network, random forest, XGBoost) are employed to predict Tapping Steel Oxygen (TSO) content, which is then used as input for the kinetic model. An optimized theoretical decarburization kinetic model is selected and validated against measured Tapping Steel Carbon (TSC) data. The key innovation lies in the integration of converter control parameters into the kinetic model through a data-driven cyclic iteration algorithm. Comparison of prediction accuracy before and after integration shows that the model’s TSC prediction within the range [−0.2, +0.2] improves by 6.26%. This work presents a novel approach for enhancing kinetic models via control parameter integration, offering effective guidance for real-time monitoring and optimization in converter steelmaking.

In business process management, the monitoring service is an important element that can prevent various problems in advance from before they occur in companies and industries. Execution log is created in an information system that is aware of the enterprise process, which helps predict the process. The ultimate goal of the proposed method is to predict the process following the running process instance and predict events based on previously completed event log data. Companies can flexibly respond to unwanted deviations in their workflow. When solving the next event prediction problem, we use a fully attention-based transformer, which has performed well in recent natural language processing approaches. After recognizing the name attribute of the event in the natural language and predicting the next event, several necessary elements were applied. It is trained using the proposed deep learning model according to specific pre-processing steps. Experiments using various business process log datasets demonstrate the superior performance of the proposed method. The name of the process prediction model we propose is “POP-ON”.

Predictive Business Process Monitoring (PBPM) is a significant research area in Business Process Management (BPM) aimed at accurately forecasting future behavioral events. At present, deep learning methods are widely cited in PBPM research, but no method has been effective in fusing data information into the control flow for multi-perspective process prediction. Therefore, this paper proposes a process prediction method based on the hierarchical BERT and multi-perspective data fusion. Firstly, the first layer BERT network learns the correlations between different category attribute data. Then, the attribute data is integrated into a weighted event-level feature vector and input into the second layer BERT network to learn the impact and priority relationship of each event on future predicted events. Next, the multi-head attention mechanism within the framework is visualized for analysis, helping to understand the decision-making logic of the framework and providing visual predictions. Finally, experimental results show that the predictive accuracy of the framework surpasses the current state-of-the-art research methods and significantly enhances the predictive performance of BPM.