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The applications of process mining, which is a business process management technique, in the field of healthcare are increasing day by day. In process mining, it is possible to analyze the process for three main purposes: discovery of the process, compliance control and process improvement, based on event logs recorded in information systems. Transforming patient-based healthcare processes data into process and event-based event logs is the first step of a process mining project in order to apply process mining techniques to human-centered, distributed, complex and multidisciplinary healthcare processes and to improve the quality of health care services. The process model discovered in multi-perspective process mining is expanded from different perspectives such as control flow, organizational, data, time and function, and the discovered process becomes more understandable. In this study, and in order to apply multi-perspective process mining, a method is proposed to convert the healthcare processes data recorded in hospital information systems into event logs. Data transformation method consists of six steps: data collection and data privacy, data integration, data conversion, data preprocessing, feature selection and extraction, and multi-perspective process mining analysis. The proposed method was validated by a case study by transforming the surgery process data of a university hospital in Turkey into an event log. The process discovery algorithm was applied to the surgery process data of the case study and the actual process was discovered, and the applicability of the data transformation method was shown on the real data. With the guiding feature of the method for healthcare professionals, it is expected to contribute to the applications of multi-perspective process mining in the field of healthcare in Turkey. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) has increasingly influenced higher education, notably in academic writing where AI-powered assisting tools offer both opportunities and challenges. Recently, the rapid growth of generative AI (GAI) has brought its impacts into sharper focus, yet the dynamics of its utilisation in academic writing remain largely unexplored. This paper focuses on examining the nature of human-AI interactions in academic writing, specifically investigating the strategies doctoral students employ when collaborating with a GAI-powered assisting tool. This study involves 626 recorded activities on how ten doctoral students interact with GAI-powered assisting tool during academic writing. AI-driven learning analytics approach was adopted for three layered analyses: (1) data pre-processing and analysis with quantitative content analysis, (2) sequence analysis with Hidden Markov Model (HMM) and hierarchical sequence clustering, and (3) pattern analysis with process mining. Findings indicate that doctoral students engaging in iterative, highly interactive processes with the GAI-powered assisting tool generally achieve better performance in the writing task. In contrast, those who use GAI merely as a supplementary information source, maintaining a linear writing approach, tend to get lower writing performance. This study points to the need for further investigations into human-AI collaboration in learning in higher education, with implications for tailored educational strategies and solutions. [ABSTRACT FROM AUTHOR]

Implementing approaches based on process mining in inter-organizational collaboration environments presents challenges related to the granularity of event logs, the privacy and autonomy of business processes, and the alignment of event data generated in inter-organizational business process (IOBP) execution. Therefore, this paper proposes a complete and modular data-driven approach that implements natural language processing techniques, text similarity, and process mining techniques (discovery and conformance checking) through a set of methods and formal rules that enable analysis of the data contained in the event logs and the intra-organizational process models of the participants in the collaboration, to identify patterns that allow the discovery of the process choreography. The approach enables merging the event logs of the inter-organizational collaboration participants from the identified message interactions, enabling the automatic construction of an IOBP model. The proposed approach was evaluated using four real-life and two artificial event logs. In discovering the choreography process, average values of 0.86, 0.89, and 0.86 were obtained for relationship precision, relation recall, and relationship F-score metrics. In evaluating the quality of the built IOBP models, values of 0.95 and 1.00 were achieved for the precision and recall metrics, respectively. The performance obtained in the different scenarios is encouraging, demonstrating the ability of the approach to discover the process choreography and the construction of business process models in inter-organizational environments. [ABSTRACT FROM AUTHOR]

Discovering the business process model from an organisation’s records of its operational processes is an active area of research in process mining. The discovered model may be used either during a new system rollout or to improve an existing system. In this paper, we present a process model discovery approach based on the recently proposed bio-inspired Manta Ray Foraging Optimization algorithm (MRFO). Since MRFO is designed to solve real-valued optimization problems, we adapted a binary version of MRFO to suit the domain of process mining. The proposed approach is compared with state-of-the-art process discovery algorithms on several synthetic and real-life event logs. The results show that compared to other algorithms, the proposed approach exhibits faster convergence and yields superior quality process models. [ABSTRACT FROM AUTHOR]

Business processes have undergone a significant transformation with the advent of the process-oriented view in organizations. The increasing complexity of business processes and the abundance of event data have driven the development and widespread adoption of process mining techniques. However, the size and noise of event logs pose challenges that require careful analysis. The inclusion of different sets of behaviors within the same business process further complicates data representation, highlighting the continued need for innovative solutions in the evolving field of process mining. Trace clustering is emerging as a solution to improve the interpretation of underlying business processes. Trace clustering offers benefits such as mitigating the impact of outliers, providing valuable insights, reducing data dimensionality, and serving as a preprocessing step in robust pipelines. However, designing an appropriate clustering pipeline can be challenging for non-experts due to the complexity of the process and the number of steps involved. For experts, it can be time-consuming and costly, requiring careful consideration of trade-offs. To address the challenge of pipeline creation, the paper proposes a genetic programming solution for trace clustering pipeline synthesis that optimizes a multi-objective function matching clustering and process quality metrics. The solution is applied to real event logs, and the results demonstrate improved performance in downstream tasks through the identification of sub-logs. [ABSTRACT FROM AUTHOR]

Background: To discover pharmacotherapy prescription patterns and their statistical associations with outcomes through a clinical pathway inference framework applied to real-world data. Methods: We apply machine learning steps in our framework using a 2006 to 2020 cohort of veterans with major depressive disorder (MDD). Outpatient antidepressant pharmacy fills, dispensed inpatient antidepressant medications, emergency department visits, self-harm, and all-cause mortality data were extracted from the Department of Veterans Affairs Corporate Data Warehouse. Results: Our MDD cohort consisted of 252,179 individuals. During the study period there were 98,417 emergency department visits, 1,016 cases of self-harm, and 1,507 deaths from all causes. The top ten prescription patterns accounted for 69.3% of the data for individuals starting antidepressants at the fluoxetine equivalent of 20-39 mg. Additionally, we found associations between outcomes and dosage change. Conclusions: For 252,179 Veterans who served in Iraq and Afghanistan with subsequent MDD noted in their electronic medical records, we documented and described the major pharmacotherapy prescription patterns implemented by Veterans Health Administration providers. Ten patterns accounted for almost 70% of the data. Associations between antidepressant usage and outcomes in observational data may be confounded. The low numbers of adverse events, especially those associated with all-cause mortality, make our calculations imprecise. Furthermore, our outcomes are also indications for both disease and treatment. Despite these limitations, we demonstrate the usefulness of our framework in providing operational insight into clinical practice, and our results underscore the need for increased monitoring during critical points of treatment. [ABSTRACT FROM AUTHOR]

The absence of a Standard Operating Procedure (SOP) can lead to many problems in operations within organisations. Process mining techniques can discover process models that reflect the actual behaviour of the process implementations by using event logs extracted from information systems. However, the process models discovered by process mining often have too many variations and deviations when compared to the actual SOPs of the processes. This study attempted to compare three prediction methods in finding a process model from process mining that has the closest properties to the actual SOP. The compared methods are Receiver Operating Characteristics (ROC), the four quality dimensions, and similarity measures for structural and behavioural similarities. For the experiment, we designed a synthetic SOP that served as a ground truth for evaluating the performance of the three prediction methods in this study. We used a synthetic event log extracted from a dummy information system we particularly built for this study to test the methods. This study’s results can be useful, e.g. for auditors to save a lot of time from conducting extensive surveys when SOPs are not readily available. [ABSTRACT FROM AUTHOR]

This paper discusses a methodology to improve the prevention processes of chronic diseases such as diabetes and strokes. The research motivation is to find a new methodological approach to design advanced Diagnostic and Therapeutic Care Pathways (PDTAs) based on the prediction of chronic disease using telemedicine technologies and machine learning (ML) data processing techniques. The aim is to decrease health risk and avoid hospitalizations through prevention. The proposed method defines a Process Mining Organization (PMO) model, managing risks using a PDTA structured to prevent chronic risk. Specifically, the data analysis is focused on stroke risk. First, we applied and compared the Random Forest (RF) and Gradient Boosted Trees (GBT) supervised algorithms to predict stroke risk, and then, the Fuzzy c-Means unsupervised algorithm to cluster information on the predicted results. The application of the proposed approach is able to increase the efficiency of healthcare human resources and drastically decrease care costs. [ABSTRACT FROM AUTHOR]

This study introduces a framework that integrates business analytics into educational decision-making to improve learner engagement and performance in Massive Open Online Courses (MOOCs), focusing on learning environments in English as a Foreign Language (EFL). By examining three specific research questions, this paper delineates patterns in learner engagement, evaluates factors that affect these patterns, and examines the relationship between these factors and educational outcomes. The study provides an empirical analysis that elucidates the connection between learner behaviors and learning outcomes by employing machine learning, process mining, and statistical methods such as hierarchical clustering, process discovery, and the Mann–Kendall test. The analysis determines that learning patterns, characterized as single-phase or multi-phase, repetitive or non-repetitive, and sequential or self-regulated, are more closely associated with the nature of the educational content—such as books, series, or reading levels—than learner characteristics. Furthermore, it has been observed that learners exhibiting self-regulated learning patterns tend to achieve superior academic outcomes. The findings advocate for integrating analytics in educational practices, offer strategic insights for educational enhancements, and propose a new perspective on the connection between learner behavior and educational success. [ABSTRACT FROM AUTHOR]

The field of predictive process monitoring focuses on case-level models to predict a single specific outcome such as a particular objective, (remaining) time, or next activity/remaining sequence. Recently, a longer-horizon, model-wide approach has been proposed in the form of process model forecasting, which predicts the future state of a whole process model through the forecasting of all activity-to-activity relations at once using time series forecasting. This paper introduces the concept of predictive process model monitoring which sits in the middle of both predictive process monitoring and process model forecasting. Concretely, by modelling a process model as a set of constraints being present between activities over time, we can capture more detailed information between activities compared to process model forecasting, while being compatible with typical predictive process monitoring objectives which are often expressed in the same language as these constraints. To achieve this, Processes-As-Movies (PAM) is introduced, i.e., a novel technique capable of jointly mining and predicting declarative process constraints between activities in various windows of a process' execution. PAM predicts what declarative rules hold for a trace (objective-based), which also supports the prediction of all constraints together as a process model (model-based). Various recurrent neural network topologies inspired by video analysis tailored to temporal high-dimensional input are used to model the process model evolution with windows as time steps, including encoder–decoder long short-term memory networks, and convolutional long short-term memory networks. Results obtained over real-life event logs show that these topologies are effective in terms of predictive accuracy and precision. [ABSTRACT FROM AUTHOR]

Goal Recognition (GR) is a research problem that studies ways to infer the goal of an intelligent agent based on its observed behavior and knowledge of the environment in which the agent operates. A common assumption of GR is that the environment is static. However, in many real-world scenarios, for example, recognizing customers' preferences, it is necessary to recognize the goals of multiple agents or multiple goals of a single agent over an extended period. Therefore, it is reasonable to expect the environment to change throughout a series of goal recognition tasks. This paper presents three process mining-based solutions to the problem of adaptive GR in a changing environment implemented as different control strategies of a system for solving standard GR problems. As a standard GR system that gets controlled, we use the system grounded in process mining techniques, as it can adjust its internal GR mechanisms based on data collected while observing the operating agents. We evaluated our control strategies over synthetic and real-world datasets. The synthetic datasets were generated using the extended version of the Goal Recognition Amidst Changing Environments (GRACE) tool. The datasets account for different types of changes and drifts in the environment. The evaluation results demonstrate a trade-off between the GR performance over time and the effort invested in adaptations of the GR mechanisms of the system, showing that few well-planned adaptations can lead to a consistently high GR performance. [ABSTRACT FROM AUTHOR]

The field of predictive process monitoring focuses on case-level models to predict a single specific outcome such as a particular objective, (remaining) time, or next activity/remaining sequence. Recently, a longer-horizon, model-wide approach has been proposed in the form of process model forecasting, which predicts the future state of a whole process model through the forecasting of all activity-to-activity relations at once using time series forecasting. This paper introduces the concept of predictive process model monitoring which sits in the middle of both predictive process monitoring and process model forecasting. Concretely, by modelling a process model as a set of constraints being present between activities over time, we can capture more detailed information between activities compared to process model forecasting, while being compatible with typical predictive process monitoring objectives which are often expressed in the same language as these constraints. To achieve this, Processes-As-Movies (PAM) is introduced, i.e., a novel technique capable of jointly mining and predicting declarative process constraints between activities in various windows of a process' execution. PAM predicts what declarative rules hold for a trace (objective-based), which also supports the prediction of all constraints together as a process model (model-based). Various recurrent neural network topologies inspired by video analysis tailored to temporal high-dimensional input are used to model the process model evolution with windows as time steps, including encoder–decoder long short-term memory networks, and convolutional long short-term memory networks. Results obtained over real-life event logs show that these topologies are effective in terms of predictive accuracy and precision. [ABSTRACT FROM AUTHOR]

The information systems supporting business processes of organizations generate and collect a large number of records in event logs that are exploitable in process mining tasks (discovery, conformance and enhancement). Under a Big Data scenario, Process Mining as a Service (PMaaS) can be attractive for organizations to outsource the storage of event logs and the processing resources for process mining tasks to the cloud in the presence of large event logs. However, the Cloud Service Provider (CSP) may be honest but curious, thus posing security and privacy risks when event log data are sensitive or subject to data privacy laws and regulations. In this work, a cryptography-based method is presented that preserves the privacy of event log data outsourced to an untrusted CSP, which executes the process discovery task, the most common task in process mining. The method conveniently encrypts the event log on the data owner’s side to enable the CSP to apply access control over the discovered models (encrypted) through proxy re-encryption. The proposed method is implemented as a software tool and validated and evaluated in terms of performance, scalability, and data utility using real medical (sensitive) data logs under recommended security levels. The results demonstrate the feasibility of the proposed approach to support Process Discovery as a Service (PDaaS), which enables privacy preservation and access control. [ABSTRACT FROM AUTHOR]

A key challenge in data mining, machine learning and artificial intelligence concerns data quality. Process mining is not an exception. A range of data quality problems exists in process data, some of which caused by activity labels. While automated techniques can be used for remedying data quality problems, they are effective up to a point and the aid of domain experts is required (cf. the "human-in-the-loop" paradigm in artificial intelligence). They are eminently suited to fix incorrect labels, but hard to engage as the repair task can be time-consuming and tedious. Gamification may offer a promising solution to this challenge. In this paper, we examine what motivational drives can be exploited to gamify activity-label repair, specifically those with identical semantics but different syntax. We conducted two experiments. First we recruited experts from the insurance domain to repair labels using our gamified system. Results show that log quality was improved, and participants had a positive experience interacting with the system. To validate the generalisability of the approach, our second experiment involved 30 participants who repaired a real-life medical log. We found promising results on quality improvement of the log and comparison with automatic approaches. This work contributes to process mining by improving activity-label quality in event logs and by turning data cleaning from the least enjoyable task to a fun experience for users. By bringing together elements from gamification, crowdsourcing ("humans-in-the-loop"), and process mining, this study contributes to improving the reliability of data-driven decision making in machine learning and artificial intelligence. • We investigated the impact of gamification on expert engagement in label repair. • We examined experts' motivations to contribute knowledge. • Two experiments were conducted in the insurance and medical domains. • We observed improvement in the quality of the event log. • Epic meaning, development, and relatedness along with other drives motivate experts. [ABSTRACT FROM AUTHOR]

Understanding how students with low socioeconomic status finance their tuition over time can help us comprehend the impact of students’ decisions on their subsequent curricular progress, graduation, or dropout. This work presents a curricular analytics approach using process mining techniques to study educational funding trajectories as processes. Specifically, the SCHOLARSHIP-LOAN-SELF-FUNDED model is designed to reveal educational funding trajectories and obtain aggregate information. Academic and tuition records of 2484 undergraduate students from a private Chilean university who started their programs with a government need- and merit-based tuition aid were analyzed. Students who lost their scholarships were more likely to drop out, whereas students who maintained this aid were more likely to graduate on time. Curricular progress per semester was slower after scholarships ended or after the students lost them and stayed. Financial aid was associated with students’ curricular progress and linked to their permanence and graduation time. Higher education institutions should consider the eligibility criteria and maintenance requirements of financial assistance when designing their curricula. [ABSTRACT FROM AUTHOR]

Nowadays universities strive to continuously enhance their educational programs to improve both the quality and quantity of their graduates. This is a sensitive problem, especially for Italian universities where only 30% of the students enrolled at the university succeed in graduating within a year after the normal duration of the study plan. Over the last few years, the Italian Ministry of University and Education has introduced several indicators to assess students' careers and help universities identify possible criticality in their study programs. However, these indicators only provide a high-level overview of the graduation process without providing insights into students' failure. To address this issue, in this work, we propose to model a study program as a process and exploit process analysis techniques to assess students' performance. These techniques allow delving into students' careers, thus enabling the investigation of their failures and delays. The findings obtained by applying our approach to the Bachelor program of an Italian university allowed us to determine common bottlenecks that seem to have an impact on students' graduation time. Moreover, we were able to determine and compare the career paths of successful and late students. The insights gathered by our analysis can be used to support university personnel in delving into factors causing some exams to be a bottleneck, as well as to determine potential improvements in the overall curricula. • Mitigating students' high failure rate is a challenge for universities. • Traditional indicators only provide a high-level view of student behavior. • Process mining techniques are used to determine bottlenecks in students' careers. • The study shows that adherence to the manifesto leads to better performance. [ABSTRACT FROM AUTHOR]

Model repair techniques aim at automatically updating a process model to incorporate behaviors that are observed in reality but are not compliant with the original model. Most state-of-the-art techniques focus on the fitness of the repaired models, with the goal of including single anomalous behaviors observed in a log in the form of the events. This often hampers the precision of the obtained models, which end up allowing much more behaviors than intended. In the quest of techniques avoiding this over-generalization pitfall, some notion of higher-level anomalous structure is taken into account. The type of structure considered is however typically limited to sequences of low-level events. In this work, we introduce a novel repair approach targeting more general high-level anomalous structures. To do this, we exploit instance graph representations of anomalous behaviors, that can be derived from the event log and the original process model. Our experiments show that considering high-level anomalies allows to generate repaired models that incorporate the behaviors of interest while maintaining precision and simplicity closer to the original model. [ABSTRACT FROM AUTHOR]

Business Process Simulation (BPS) is a common approach to estimate the impact of changes to a business process on its performance measures. For example, it allows us to estimate what would be the cycle time of a process if we automated one of its activities, or if some resources become unavailable. The starting point of BPS is a business process model annotated with simulation parameters (a BPS model). In traditional approaches, BPS models are manually designed by modeling specialists. This approach is time-consuming and error-prone. To address this shortcoming, several studies have proposed methods to automatically discover BPS models from event logs via process mining techniques. However, current techniques in this space discover BPS models that only capture waiting times caused by resource contention or resource unavailability. Oftentimes, a considerable portion of the waiting time in a business process corresponds to extraneous delays, e.g., a resource waits for the customer to return a phone call. This article proposes a method that discovers extraneous delays from event logs of business process executions. The proposed approach computes, for each pair of causally consecutive activity instances in the event log, the time when the target activity instance should theoretically have started, given the availability of the relevant resource. Based on the difference between the theoretical and the actual start times, the approach estimates the distribution of extraneous delays, and it enhances the BPS model with timer events to capture these delays. An empirical evaluation involving synthetic and real-life logs shows that the approach produces BPS models that better reflect the temporal dynamics of the process, relative to BPS models that do not capture extraneous delays. [ABSTRACT FROM AUTHOR]

The advancement of the maritime industry towards technologically integrated and automated systems has significantly increased the complexity of onboard Industrial Control Systems (ICS), raising concerns about cybersecurity risks. In this paper, we examine typical onboard ICS configurations through an adversarial lens. We introduce a threat model that leverages domain-specific peculiarities, e.g., maritime protocols, and targets vulnerability vectors to execute software attacks against the infrastructures of shipboard ICS. This includes a case study on a critical subsystem of ship machinery: the steering gear system. We have developed a novel attack methodology intended for use by targeted malware. A comprehensive experimental assessment confirms the feasibility of attacks devised according to our methodology. [ABSTRACT FROM AUTHOR]

Conformance checking techniques compare how a process is supposed to be executed according to a model with how it is executed in reality according to an event log. Alignment-based approaches are the most successful solutions for conformance checking. Optimal alignments are a way of finding the best match between the real and the modeled behavior and identifying the differences. However, finding these optimal alignments is a challenging task, especially for complex cases where the log and the model have many events and paths. The difficulty lies in the computational complexity required to find these alignments. To address this problem, we propose an efficient algorithm named REACH based on the A* search algorithm. The core components of the proposal are the use of a partial reachability graph for faster execution of process models for alignment computation and a set of optimization techniques for reducing the number of states explored by the A* algorithm. These improve performance by both reducing the required computation time per state and the number of states to process respectively. To evaluate the performance and scalability, we conducted tests using 227 pairs of logs and models, comparing the results obtained with those from 10 state-of-the-art approaches. Results show that REACH outperforms the other proposals in runtimes, and even aligns logs and models that no other algorithm is able to align. • We present REACH, an efficient, alignment-based, optimal, conformance checking tool. • We propose new ways for reducing the number of states explored by the A* algorithm. • It introduces an incremental reachability graph for efficient model execution. • We test it using 227 varied log-model pairs, comparing it with 10 recent algorithms. • It outperforms the other approaches, even solving problems that no other tool can. [ABSTRACT FROM AUTHOR]

The continuous evolution of digital technologies applied to the more traditional world of industrial automation led to Industry 4.0, which envisions production processes subject to continuous monitoring and able to dynamically respond to changes that can affect the production at any stage (resilient factory). The concept of agility, which is a core element of Industry 4.0, is defined as the ability to quickly react to breaks and quickly adapt to changes. Accurate approaches should be implemented aiming at managing, optimizing and improving production processes. In this vision paper, we show how process management (BPM) can benefit from the availability of raw data from the industrial internet of things to obtain agile processes by using a top-down approach based on automated synthesis and a bottom-up approach based on mining. [ABSTRACT FROM AUTHOR]

Anomaly detection plays a critical role in ensuring the quality and safety of industrial processes. Process mining, as an emerging technology, has proven effective in extracting knowledge and process rules inherent in process events. However, industrial time series data possess characteristics such as high noise, and data redundancy, posing challenges for accurately assessing system anomalies using traditional data-driven methods. To address these challenges, this paper proposes a Trend-Value Fusion Process Mining (TVPM) approach based on the representation of industrial process events to tackle anomaly detection in industrial processes. Firstly, TVPM introduces a fusion measure that combines data trends and mean-value to provide a comprehensive description of operating events in industrial processes. This fusion measure serves as a vital tool for capturing the complex details and features of industrial events. Additionally, a novel process discovery algorithm is developed to extract behavioral patterns and sequence information from events. Finally, a process abnormal state detection model is constructed by leveraging the comprehensive and in-depth information obtained from TVPM. The proposed method is applied to an industrial coal gasification process, yielding satisfactory results. • An approach to TVPM of industrial process operating events for abnormal detection was proposed. • This paper introduced a method for extracting process event constructs that blend value and trend information. • The final process abnormal detection framework was applied to an actual industrial process. [ABSTRACT FROM AUTHOR]

Process mining is a relatively new discipline that focuses on gaining process-centric knowledge from event logs collected by enterprise systems. From an academic standpoint, there has been a constant effort to develop various techniques to automatically discover process models, analyze the compliance of real-life processes to the process models, predict operational frictions, and recommend possible actions to mitigate emerging risks. As far as applications are concerned, process mining techniques have been adopted in various industries, such as healthcare, manufacturing, logistics, and finance. In this work, we analyze the process mining literature in-depth using text mining and machine learning techniques. More in detail, we (1) analyze the main research fields in process mining and their trends, (2) investigate the relationship between the fields, and (3) predict the expansion of the fields in the near future. To that end, we analyze 2,677 process mining articles from 2003 to 2022 using a range of techniques such as topic modeling with the pre-trained language model, exploratory bibliometric analysis, network and community detection, and future prediction. • Process mining focuses on deriving process-oriented knowledge from event logs. • A method for analyzing process mining literature using machine learning is proposed. • There are three process mining research areas: technique, application, and support. • Process discovery and conformance checking dominantly have occupied a large part. • Prediction-based and object-centric process mining belongs to skyrocketing groups. [ABSTRACT FROM AUTHOR]

Most contemporary process discovery methods take as inputs only positive examples of process executions, and so they are one-class classification algorithms. However, we have found negative examples to also be available in industry, hence we build on earlier work that treats process discovery as a binary classification problem. This approach opens the door to many well-established methods and metrics from machine learning, in particular to improve the distinction between what should and should not be allowed by the output model. Concretely, we (1) present a verified formalisation of process discovery as a binary classification problem; (2) provide cases with negative examples from industry, including real-life logs; (3) propose the Rejection Miner binary classification procedure, applicable to any process notation that has a suitable syntactic composition operator; (4) implement two concrete binary miners, one outputting Declare patterns, the other Dynamic Condition Response (DCR) graphs; and (5) apply these miners to real world and synthetic logs obtained from our industry partners and the process discovery contest, showing increased output model quality in terms of accuracy and model size. • We formalise process discovery as a binary classification problem. • We automatically verified this formalisation using the Isabelle proof assistant. • We propose the notation-agnostic binary Rejection Miner discovery algorithm. • We provide real-world examples of labelled event logs. • We provide two Rejection Miner prototypes for DCR Graphs and Declare. [ABSTRACT FROM AUTHOR]

Previous studies investigating the efficacy of long short-term memory (LSTM) recurrent neural networks in predictive process monitoring and their ability to capture the underlying process structure have raised concerns about their limited ability to generalize to unseen behavior. Event logs often fail to capture the full spectrum of behavior permitted by the underlying processes. To overcome these challenges, this study introduces innovative validation set sampling strategies based on control-flow variant-based resampling. These strategies have undergone extensive evaluation to assess their impact on hyperparameter selection and early stopping, resulting in notable enhancements to the generalization capabilities of trained LSTM models. In addition, this study expands the experimental framework to enable accurate interpretation of underlying process models and provide valuable insights. By conducting experiments with event logs representing process models of varying complexities, this research elucidates the effectiveness of the proposed validation strategies. Furthermore, the extended framework facilitates investigations into the influence of event log completeness on the learning quality of predictive process models. The novel validation set sampling strategies proposed in this study facilitate the development of more effective and reliable predictive process models, ultimately bolstering generalization capabilities and improving the understanding of underlying process dynamics. [ABSTRACT FROM AUTHOR]

These days, businesses keep track of more and more data in their information systems. Moreover, this data becomes more fine-grained than ever, tracking clicks and mutations in databases at the lowest level possible. Faced with such data, process discovery often struggles with producing comprehensible models, as they instead return spaghetti-like models. Such finely granulated models do not fit the business user's mental model of the process under investigation. To tackle this, event log abstraction (ELA) techniques can transform the underlying event log to a higher granularity level. However, insights into the performance of these techniques are lacking in literature as results are only based on small-scale experiments and are often inconclusive. Against this background, this paper evaluates state-of-the-art abstraction techniques on 400 event logs. Results show that ELA sacrifices fitness for precision, but complexity reductions heavily depend on the ELA technique used. This study also illustrates the importance of a larger-scale experiment, as sub-sampling of results leads to contradictory conclusions. [ABSTRACT FROM AUTHOR]

With the advent of smart manufacturing, companies are adapting their business processes and procedures to match concepts and requirements related to this industrial revolution. In this scenario, maintenance sector should evolve to keep pace with new technological trends by shifting from traditional approaches to a smart paradigm. Some consolidated methodologies rely on standard evaluation, like Risk Priority Number, to plan maintenance actions on machines and components. However, contemporary and smart approaches should be characterized by data-driven (quantitative knowledge) as well as operator’s experience (qualitative knowledge) in order to provide a whole understanding and visibility of the system health status. In this context and with a view of digital twin concepts (under the Industry 4.0 technology), a decision support methodology (or framework) plays an essential role by providing knowledge and assisting operators in the decision-making process. With this concept in mind, this paper focuses on integrating several and not alike fields of study (process mining, multicriteria decision-making (MCDM), and data fusion) to enable the assessment of a risk and criticality analysis to rank industrial machines in order to carry out/plan maintenance tasks on them. Therefore, merging those fields of study aims at accomplishing a dynamic, flexible, and real-time evaluation to perform a robust, accurate, and responsive analysis of the system. The results from different analysis scenarios show the influence of maintenance indicators on the machine ranking process, highlighting the feasibility and flexibility of the approach, as well as expanding the application in modern systems by optimizing quality of maintenance decisions. [ABSTRACT FROM AUTHOR]

Process mining is an interdisciplinary field that combines Artificial Intelligence and Business Process Management to extract insights from historical event data. Knowledge-intensive processes, which predominantly involve knowledge work, are often inadequately monitored by process-aware information systems. Consequently, the event data necessary for applying process mining techniques are frequently unavailable. Emails are widely used in knowledge-intensive processes for scheduling meetings, sharing documents, and reporting on the completion of outstanding tasks, which makes them suitable candidates for replacing event logs as the primary data source. In this work, we focus on the task of extracting the set of next recommended activities from incoming emails. Yet, we face two major challenges. Firstly, emails do not express process information explicitly but rather contain subtext that implies what the next best actions would be. Secondly, email data lacks domain-specific labels that would enable the use of machine learning. We overcome these limitations by utilizing an email taxonomy to represent user intents, thus bridging the gap between textual information and process semantics, as well as leveraging pre-trained transformer models applied in zero-shot and few-shot settings that require little to no labeled email data. An evaluation of our method on real-world unlabeled email communications demonstrates its effectiveness in recognizing intents and extracting activities. [ABSTRACT FROM AUTHOR]

Event sequence data is increasingly available. Many business operations are supported by information systems that record transactions, events, state changes, message exchanges, and similar elements. This observation also applies to various industries, including production, logistics, healthcare, financial services, and education. The variety of application areas explains that techniques for event sequence data analysis have been developed rather independently in different fields of computer science. Most prominent are contributions from information visualization and from process mining. So far, the contributions from these two fields have neither been compared nor have they been mapped to an integrated framework. Such intransparency is problematic since it bears the risk of opportunities of integration are missed and concepts established in one field are independently reinvented in the other one. In this paper, we develop the Event Sequence Visualization framework (ESeVis) that gives due credit to the traditions of both fields. Our mapping study provides an integrated perspective on both fields and identifies potential for synergies for future research. • We propose the ESeVis Framework to categorize visualizations of event sequence data. • We review the literature of event sequence data visualizations. • Instance representations of event sequences are mostly from information visualization. • Process mining focuses on generating formal models. • Potential for synergy of process mining and information visualization. [ABSTRACT FROM AUTHOR]

Declarative process specifications define the behavior of processes by means of rules based on Linear Temporal Logic on Finite Traces LTL f. In a mining context, these specifications are inferred from, and checked on, multi-sets of runs recorded by information systems (namely, event logs). To this end, being able to gauge the degree to which process data comply with a specification is key. However, existing mining and verification techniques analyze the rules in isolation, thereby disregarding their interplay. In this paper, we introduce a framework to devise probabilistic measures for declarative process specifications. Thereupon, we propose a technique that measures the degree of satisfaction of specifications over event logs. To assess our approach, we conduct an evaluation with real-world data, evidencing its applicability for diverse process mining tasks, including discovery, checking, and drift detection. • Interestingness measures for rule-based process specifications are introduced. • A probabilistic estimation framework of single rules and entire specifications is proposed. • Measures of specifications differ from the aggregation of the measures for their individual rules. • Measures exhibit different sensitivity to process behavior changes over time. [ABSTRACT FROM AUTHOR]

Business process simulation is a versatile technique to predict the impact of one or more changes on the performance of a process. Process simulation is often used to identify sets of changes that optimize one or more performance measures. Mainstream approaches to process simulation suffer from various limitations, some stemming from the fact that they treat resources as undifferentiated entities grouped into resource pools, and then assuming that all resources in a pool have the same performance and share the same availability calendars. Previous studies have acknowledged these assumptions, without quantifying their impact on simulation model accuracy. This article addresses this gap in the context of simulation models automatically discovered from event logs. Specifically, the contribution of the article is three-fold. First, the article proposes a simulation approach, wherein each resource is treated as an individual entity, with its own performance and availability calendar. Second, it proposes a method for discovering simulation models with differentiated performance and availability, starting from an event log of a business process. Third, it proposes a method to optimize the resource availability calendars in order to minimize resource cost while also minimizing cycle times. An empirical evaluation shows that simulation models with differentiated resources more closely replicate the distributions of cycle times and the work rhythm in a process than models with undifferentiated resources, and that iteratively optimizing resource allocations in conjunction with resource calendars leads to superior cost–time tradeoffs with respect to optimizing these allocations and calendars separately. [ABSTRACT FROM AUTHOR]

Process mining can help organizations by extracting knowledge from event logs. However, process mining techniques often assume business processes are stationary, while actual business processes are constantly subject to change because of the complexity of organizations and their external environment. Thus, addressing process changes over time – known as concept drifts – allows for a better understanding of process behavior and can provide a competitive edge for organizations, especially in an online data stream scenario. Current approaches to handling process concept drift focus primarily on detecting and locating concept drifts, often through an integrated, albeit offline, approach. However, part of these integrated approaches rely on complex data structures related to tree-based process models, usually discovered through algorithms whose results are influenced by specific heuristic rules. Moreover, most of the proposed approaches have not been tested on public true concept drift-labeled event logs commonly used as benchmark, making comparative analysis difficult. In this article, we propose an online approach to detect and localize concept drifts in an integrated way using batch and stream trace clustering support. In our approach, cluster models provide input information for both concept drift detection and localization methods. Each cluster abstracts a behavior profile underlying the process and reveals descriptive information about the discovered concept drifts. Experiments with benchmark synthetic event logs with different control-flow changes, as well as with real-world event logs, showed that our approach, when relying on the same clustering model, is competitive in relation to baselines concept drift detection method. In addition, the experiment showed our approach is able to correctly locate the concept drifts detected and allows the analysis of such concept drifts through different process behavior profiles. [ABSTRACT FROM AUTHOR]