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The increasing development of the competitive market has forced organisations to make great efforts in the processes of supply, production, and distribution to meet customer demand in the shortest time and at the lowest cost. A cross-docking (CD) system is one of the successful and practical strategies in this field considered by researchers in various fields. Also, business process management plays a key role in continuous improvement and increased productivity. In today's digital age, due to the ability to record all activities, process mining is an important method to identify the current situation and improve productivity. In this research, a newly established CD belonging to a chain store is studied to improve the current situation, in which different goods enter and then exit after different processes. The purpose of this study is to obtain the optimal number of doors and loaders as sources. First, helping an RFID system, all activities are recorded, and the current situation of the processes is monitored, and then, the real process model is identified using heuristic and inductive miner algorithms. After adapting to the event log by using the simulation process in Arena software, different scenarios are examined, and the best possible case is presented. [ABSTRACT FROM AUTHOR]

Production systems have to be adapted continuously to changing circumstances. This means that the responsible production planner has to frequently make decisions about reconfiguring complex systems under uncertainty whose outcome greatly affects the company's business success. Discrete-event simulation is one powerful tool to support the necessary analysis and scenario evaluation, but still remains time-consuming and tricky to set up and to maintain. When implemented as a digital twin of the production system, the simulation model can maintain a high degree of accuracy over a long time period. An approach to realise this potential is presented and illustrated in this paper with a use case from the automotive industry. This paper contributes several new methods and findings to the development of digital twins of production systems: Firstly, it demonstrates how exceptional events in the validation of the digital twin can be handled. Secondly, it shows how structural changes in the system can be discovered using data on machine activity and process mining. Thirdly, the paper introduces a possibility on how to assess the accuracy of the digital twin. Furthermore, it demonstrates how to assess the robustness of the digital twin to estimation errors in machine processing times. [ABSTRACT FROM AUTHOR]

Owing to increasingly important drivers for change, such as automation, digitisation, and dynamic demand patterns, simulation models of production systems become outdated rapidly. At the same time, building simulation models often requires much time, cost, and expertise, especially when dealing with complex job shop production systems. To address these challenges, an automated simulation model generation (ASMG) framework for material flow simulation of production systems is presented. This framework contains multiple approaches to infer routeing, control and temporal aspects from event-based data. To achieve this, methods from process mining (PM) and machine learning (ML) are applied. Additionally, the suitability of Coloured Petri Nets (CPNs) to serve as conceptual and operational simulation models is examined. The inferred simulation models have high validity when compared to the real system concerning the KPIs machine utilisation, throughput, and work in process. It is shown, that most model elements can be inferred very well, in particular process routes, processing times, and resource selection rules. This proof of concept serves as a foundation for research on detection approaches inferring further model elements such as setup times accurately. [ABSTRACT FROM AUTHOR]

This study investigates the relationship between students' self-efficacy beliefs, goal-setting, and learning tactics in an online business course. Using sequence analysis and process mining techniques, we analyzed log data from 209 students to identify distinct learning tactics and their association with self-efficacy beliefs, inferred from initial goal-setting and final grades. Four learning tactics were identified, with significant differences observed between students with high and low self-efficacy beliefs. High self-efficacy students demonstrated more varied and adaptive tactics, including greater use of quizzes and project-focused activities. In contrast, low self-efficacy students and those who didn't set goals showed less engagement and fewer monitoring activities. The project-focused tactic had the strongest correlation with final grades and goal achievement. Goal-setting at the course's start was linked to more effective learning behaviors and better outcomes. The study reveals how students' online learning behavior changes based on initial goal-setting and subsequent reflection. These findings contribute to research on self-regulated learning in online environments and offer practical implications for designing online courses and learning analytics interventions. Understanding these differences enables the development of targeted interventions to improve learning tactics and self-efficacy beliefs among students, ultimately enhancing their ability to achieve learning goals in online educational settings. [ABSTRACT FROM AUTHOR]

Port logistics is a highly human-centered, complex and flexible business process. Existing research on process mining has the problem of automatically discovering low quality process models, and lacks support for systematic root cause analysis of process deviations, which results in a weak ability to analyze process deviations in ports. To address this issue, this paper proposed a process mining method framework for root cause analysis of process deviation in port logistics. The method was based on event logs for port logistics activity statistics, used Split Miner and Inductive Miner to discover the actual process model, used the alignment method to identify the process deviation, combined the classification decision tree and Sankey diagram to conduct root cause analysis on process deviations. Through the practical application in a large port logistics pick-up process, the results show that the method can discover more details of the actual execution process to provide support for the root cause analysis of process deviation, and carry out a systematic root cause analysis of process deviation. The method strengthens the capability of analyzing process deviations in port logistics, which in turn reduces the risk of port logistics management. [ABSTRACT FROM AUTHOR]

Predictive monitoring is a subfield of process mining that aims to predict how a running case will unfold in the future. One of its main challenges is forecasting the sequence of activities that will occur from a given point in time —suffix prediction—. Most approaches to the suffix prediction problem learn to predict the suffix by learning how to predict the next activity only, while also disregarding structural information present in the process model. This paper proposes a novel architecture based on an encoder-decoder model with an attention mechanism that decouples the representation learning of the prefixes from the inference phase, predicting only the activities of the suffix. During the inference phase, this architecture is extended with a heuristic search algorithm that selects the most probable suffix according to both the structural information extracted from the process model and the information extracted from the log. Our approach has been tested using 12 public event logs against 6 different state-of-the-art proposals, showing that it significantly outperforms these proposals. [ABSTRACT FROM AUTHOR]

We designed, implemented, and evaluated a curriculum that trains accounting students and professionals in process mining, an important emerging technology that is not extensively taught in most degree programs. We partnered with a leading provider of process mining software to create the "Process Mining Audit Professional Badge." The badge's curriculum combines modules focused on generic process mining skills and modules focused on process mining applications in auditing settings. In 22 months, 1,532 individuals completed the curriculum. Learners were highly satisfied with the training, ranked it among the most effective training modalities they had experienced, and perceived that they learned a great deal about both process mining and the auditing process. We demonstrate an approach to developing materials on emerging technologies that is perceived as useful by learners. If adopted more broadly, the approach could facilitate curricular integration of skills demanded by the profession. [ABSTRACT FROM AUTHOR]

Collaborative Problem Solving (CPS) has received increasing attention for its role in promoting learners' cognitive and social development in STEM education. However, little is known about how learners interact dynamically within a group at different time granularities. This gap mainly resulted from overlooking the time dimension of interactions, leading to a lack of nuanced understanding of moment-to-moment interaction in CPS. In this study, we demonstrated the potential of temporal group interaction density in modeling online CPS interactions and investigated the impact of temporal interaction density on CPS processes and outcomes. Specifically, we proposed using cumulative weighted density to measure the holistic state of group interactions and explained the differences in group interactions with different collaborative performance and interaction densities by modeling the transition and evolution of interaction sequences through Apriori and cumulative relative centrality. Results indicated that group interaction density cannot directly predict their collaborative performance, but notable differences in interaction patterns existed in the high-performance groups with different interaction densities, while low-performance groups showed interactive commonalities towards the completion of CPS. The findings of this study guided the design of CPS interventions and supported the process mining of CPS interactions, with vital practical implications for CPS assessment and skills development. [ABSTRACT FROM AUTHOR]

Process discovery aims to discover process models from event logs to describe actual business processes. The quality of event logs has an impact on the quality of process models, so preprocessing methods can be used to improve the quality of event logs. Chaotic activities may exist in real business scenarios, and the occurrence of chaotic activities is independent of other activities in the process and can occur at any location in the event log at any frequency. Therefore, chaotic activities seriously affect the model quality of process discovery. Filtering chaotic activities in event logs can effectively improve the quality of event logs and thus improve the quality of process models. The traditional chaotic activity filtering algorithm makes it difficult to balance accuracy and time performance. Therefore, a direct method for filtering chaotic activities is proposed in this paper. By analyzing the relationship between activities, chaotic activities are identified in the log according to the characteristics of chaotic activities and the direct following relationship of activities as the judgment condition, and the filtering of chaotic activities in the event log is realized. In addition, this paper proposes an indirect chaotic activity filtering method, which identifies and filters chaotic activities in the log by analyzing the influence of the existence of different activities on the overall chaos degree of the log. The proposed method is compared with the traditional chaotic activity filtering method on several simulation/real data sets, and the accuracy and running time between the multi-group event logs and the process models generated before and after chaotic activity filtering are analyzed, further verifying the effectiveness and feasibility of the proposed method. By summarizing the experimental results, it is found that the accuracy of the proposed chaotic activity filtering methods is greater than that of the frequency-based filtering method and is close to that of the entropy-based chaotic activity filtering methods. Moreover, compared with other filtering methods used in the experiment, the chaotic activity filtering method proposed in this paper can improve the efficiency by 23.4% on average for simulation logs, and by 84.25% on average for real event logs. It is concluded that compared with other filtering methods, the proposed chaotic activity filtering methods have higher accuracy and can effectively improve the time performance of chaotic activity filtering. Therefore, the chaotic activity filtering method proposed in this paper can balance the accuracy and time performance, and can ensure the integrity of the filtered event log to a certain extent. [ABSTRACT FROM AUTHOR]

Background Objectives Methods Results and Conclusions Both learning and problem solving are major goals of complex problem solving in engineering education. The order of knowledge construction and problem solving in learning through problem solving, however, has not been explained in current literature.To understand their relationships, this study compared the effects of different goal type orders on collaborative simulation‐based inquiry in engineering problem solving.In the study, 24 engineering undergraduate students worked in pairs to solve a wind tunnel problem using a computer simulation application. Process mining and epistemic network analysis were used to model the inquiry process and identify inquiry patterns based on the log data and discussion discourse.The results showed the goal type of first assigned subtask influenced the inquiry strategies of medium‐performance learners most. In addition, the goal type of first assigned subtask had influence on learners' inquiry process. The learners assigned with a problem‐solving goal subtask first (Problem solving goal‐Learning goal condition) had more simulation trials and centered their inquiry discourse around clarification while the learners assigned with a learning goal subtask first (Learning goal‐Problem solving goal condition) connected reflection with other inquiry phases more often. The findings of this study suggested assigning a learning goal subtask first in simulated‐based complex engineering problem solving could be used as an idea‐centered scaffolding, especially for medium‐performance learners. However, low‐performance learners would experience a “situate knowledge paradox” and needs extra guidance. It is important to monitor the knowledge construction process in complex problem solving, even when seeking a solution is the major task. [ABSTRACT FROM AUTHOR]

In process mining, conformance checking determines the quality of discrete‐event process models through several measures computed with respect to a set of event sequences λ from which the model N is obtained. Precision is a measure that evaluates the surplus language of the model LN with respect to λ. This article outlines four precision evaluation approaches for Petri net (PN) workflow models obtained through process discovery techniques. First, the basics of the quality measures of process models stablished in conformance checking are summarised. Then, the methods for model precision evaluation in literature are outlined and analysed. Finally, the advantages and limitations of the methods are pointed out and compared. [ABSTRACT FROM AUTHOR]

Designers working on large-scale complex engineering projects may encounter design errors due to limited experience and expertise. Therefore, during the design process, accurately identifying the designer's design intention and recommending relevant design knowledge can enhance design efficiency and reduce errors. In this paper, we propose a closed-loop human–computer interactive design method based on sequential human intention prediction and knowledge recommendation. This method leverages the Function-Behaviour-Structure (FBS) model to reduce the dimensionality of design action sequences, so as to facilitate the analysis of potential design patterns. The processed action sequences are used to train Transformer to predict design intentions. In different input sequences, Transformer achieved a highest prediction accuracy of 92.09%. We construct a Design Knowledge Recommendation Framework (DKRF) and its corresponding design knowledge matching algorithm. This framework accurately recommends design knowledge to designers, solving design stagnation and improving design efficiency. Finally, a case study of four types of mechanical model design is conducted to demonstrate the feasibility and wide applicability of the proposed closed-loop human–computer interactive design method. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a web-based system that leverages the capabilities of the ρ(rho)-algorithm, which is a Structure Information Control Net (SICN)-oriented process mining algorithm, with open-source platforms, including Django, Graphviz, and Cytoscape, to facilitate the rediscovery and visualization of business process models. Our approach involves discovering SICN-oriented process models from process instances from the IEEE XES-formatted process enactment event logs dataset. This discovering process is facilitated by the ρ-algorithm, and visualization output is transformed into either a JSON or DOT formatted file, catering to the compatibility requirements of Cytoscape or Graphviz, respectively. The proposed system utilizes the robust Django platform, which enables the creation of a user-friendly web interface. This interface offers a clear, concise, modern, and interactive visualization of the rediscovered business processes, fostering an intuitive exploration experience. The experiment conducted on our proposed web-based process discovery system demonstrates its ability and efficiency showing that the system is a valuable tool for discovering business process models from process event logs. Its development not only contributes to the advancement of process mining but also serves as an educational resource. Readers, students, and practitioners interested in process mining can leverage this system as a completely free process miner to gain hands-on experience in rediscovering and visualizing process models from event logs. [ABSTRACT FROM AUTHOR]

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As urbanization accelerates and population structures change, healthcare buildings are becoming increasingly crowded. Predicting functional area demand is crucial to adapting to this trend and providing high-quality services. This paper introduces an innovative multi-stage method to forecast unbuilt projects using operational data from hospital information systems and building design models to automatically calculate spatial demands. The study's main findings demonstrate that our method successfully outputs regional demand data, supporting hospital design validation and operational decision-making. Through processing and analyzing log data, this research identified the dynamic characteristics of user activities in hospital buildings and converted them into a time series data format. This method has iterative self-validation and self-optimization features and can maintain flexibility in different scenarios and frequently changing design drawings. This method will provide technical support for a wide range of hospital building stakeholders and has the potential to be applied to more types of buildings. These findings will contribute to various fields, including medical facility planning, design, and public health. [ABSTRACT FROM AUTHOR]

With the rapid expansion of process mining implementation in global enterprises distributed across numerous branches, there is a critical requirement to develop an application qualified for real-time operation with fast and precise data integration. To address this challenge, computational parallelism emerges as a feasible solution to accelerate data analytics, with graphical processor unit (GPU) computing currently trending for achieving parallelism acceleration. In this study, we developed a process mining application to optimize parallel and distributed process discovery through a combination of central processing unit (CPU) and GPU computing. The use of this computing combination is leveraged for executing multi-windowing threads within multi-instruction, multiple data (MIMD) in the CPU for streaming distributed event logs, using multi-instruction, single data (MISD) within the CPU to deploy a large footprint pipeline to the GPU, and then utilizing single instruction, multiple data (SIMD) to execute global thread discovery within the GPU. This method significantly accelerates performance in real-time distributed discovery. By reducing branch divergence in SIMD on the global thread GPU parallelism, it outperformed local-thread CPU execution in deterministic discovery, speeding up from 10 to 40 times under specific conditions using a novel min-max flag algorithm implemented within the main steps of the process discovery. [ABSTRACT FROM AUTHOR]

The introduction of the Industrial Internet of Things (IIoT) has led to major changes in the industry. Thanks to machine data, business process management methods and techniques could also be applied to them. However, one data source has so far remained untouched: The network data of the machines. In the business environment, process mining, for example, has already been carried out based on network data, but the IIoT, with its particular protocols such as OPC UA, has yet to be investigated. With the help of design science research and on the shoulders of CRISP-DM, we first develop a framework for process mining in the IIoT in this paper. We then apply the framework to real-world IIoT network traffic data and evaluate the outcome and performance of our approach in detail. We find tremendous potential in network traffic data but also limitations. Among other things, due to the dependence on process experts and the existence of case IDs. [ABSTRACT FROM AUTHOR]

Delays in legal proceedings significantly impact both corporate finances and individual livelihoods. Traditional methods for managing these delays typically rely on subjective assessments of what constitutes a reasonable process duration. This study explores a more precise approach by integrating machine learning and process mining techniques to enhance prediction of legal proceedings' overall time. Diverging from previous works that either utilized machine learning analysis or process mining in isolation, this research synergizes these approaches. We applied process mining clustering techniques to over 60,000 cases from Brazilian labor courts to segment cases based on their procedural movements, creating clusters. These clusters, along with other procedural characteristics, such as case subject, class, and digital status, were then incorporated into a feature set for regression modelling. We employed linear regression, support vector regressor, and gradient boosting based methods to develop models that predicted case duration. The gradient boosting model demonstrated the best performance with an R 2 -score of 0.87. Furthermore, our analysis identifies time bands where the model performs better and employs explainable AI techniques to elucidate key features influencing case durations. The clustering features emerged among the most significant for the task. The proposed combined approach offers a comprehensive method for analyzing and forecasting legal case timelines and also shows the potential of process mining clustering techniques to improve the analysis. [ABSTRACT FROM AUTHOR]

Some Business Process Management Systems (BPMSs) have been developed in the field of smart factories. These systems are typically based on technical or production areas and technical processes. However, many existing systems, with respect to technologies used in smart factories and also the dynamic nature of the processes in these environments, are not able meet requirements of smart factories in the business process execution. The present study presents a new prototype of BPMS architecture based on smart factories' characteristics. This prototype has several components. In the monitoring component, process management can take place through process mining techniques inside a defined data analysis system for collecting event logs from big data. This component could operate based on control and optimization modules. The control module is applied to discover process models and their conformity with models extracted from business process analysis using Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Adaptive Boosting (AdaBoost) algorithms. Also, the optimization module can improve the processes model based on Business Process Intelligence (BPI) technique and Key Performance Indicators (KPIs). The results of the new prototype execution on a case study indicate that the proposed architecture is highly accurate, complete, and optimal in process management for smart factories. [ABSTRACT FROM AUTHOR]

Learning analytics dashboards (LADs) are often used to display real-time data indicating student learning trajectories and outcomes. Successful use of LADs requires teachers to orient their dashboard reviews with clear goals, apply appropriate strategies to interpret visualized information on LADs and monitor and evaluate their interpretations to meet goals. This process is known as self-regulated learning (SRL). Critical as it is, little research investigates teachers' SRL in LAD usage. The present study addressed the gap by examining teachers' SRL and sought to understand how teachers' SRL relates to their use of LADs. To this end, a case study was designed in which ten participants were invited to use a LAD for asynchronous online problem-based learning. Think-aloud techniques and process mining methods were applied. The findings show that teachers were cognitive regulation in the early stage of LAD usage and became more metacognitive regulated later. The comparison of SRL between the good and the weak regulators indicates that the good self-regulators enacted more monitoring and evaluation events. Thus their regulator pattern was more non-linear. The qualitative analysis of think-aloud protocols reveals that teachers with good SRL are more likely to use the LAD to diagnose issues in student learning and collaboration. The study highlights the importance of SRL for teachers' success in using LAD for data-driven instructions. The study also reinforces the importance of fostering teachers' SRL, which accounts for teachers' professional success in the digital era. [ABSTRACT FROM AUTHOR]

Abstract This study investigates the relationship between students' self-efficacy beliefs, goal-setting, and learning tactics in an online business course. Using sequence analysis and process mining techniques, we analyzed log data from 209 students to identify distinct learning tactics and their association with self-efficacy beliefs, inferred from initial goal-setting and final grades. Four learning tactics were identified, with significant differences observed between students with high and low self-efficacy beliefs. High self-efficacy students demonstrated more varied and adaptive tactics, including greater use of quizzes and project-focused activities. In contrast, low self-efficacy students and those who didn't set goals showed less engagement and fewer monitoring activities. The project-focused tactic had the strongest correlation with final grades and goal achievement. Goal-setting at the course's start was linked to more effective learning behaviors and better outcomes. The study reveals how students' online learning behavior changes based on initial goal-setting and subsequent reflection. These findings contribute to research on self-regulated learning in online environments and offer practical implications for designing online courses and learning analytics interventions. Understanding these differences enables the development of targeted interventions to improve learning tactics and self-efficacy beliefs among students, ultimately enhancing their ability to achieve learning goals in online educational settings.