Event Log Data Research Articles

The number of patient scheduled hours resulting in a 40-hour work week by physician specialty and setting: a cross-sectional study using electronic health record event log data.

To quantify how many patient scheduled hours would result in a 40-h work week (PSH40) for ambulatory physicians and to determine how PSH40 varies by specialty and practice type. We calculated PSH40 for 186188 ambulatory physicians across 395 organizations from November 2021 through April 2022 stratified by specialty. Median PSH40 for the sample was 33.2 h (IQR: 28.7-36.5). PSH40 was lowest in infectious disease (26.2, IQR: 21.6-31.1), geriatrics (27.2, IQR: 21.5-32.0) and hematology (28.6, IQR: 23.6-32.6) and highest in plastic surgery (35.7, IQR: 32.8-37.7), pain medicine (35.8, IQR: 32.6-37.9) and sports medicine (36.0, IQR: 33.3-38.1). Health system leaders and physicians will benefit from data driven and transparent discussions about work hour expectations. The PSH40 measure can also be used to quantify the impact of variations in the clinical care environment on the in-person ambulatory patient care time available to physicians. PSH40 is a novel measure that can be generated from vendor-derived metrics and used by operational leaders to inform work expectations. It can also support research into the impact of changes in the care environment on physicians' workload and capacity.

Learner Assessment System in e-Learning with OBE Approach: Activity Performance, Ability Level and Recommendation

E-learning can lead learners to achieve learning outcomes if it is designed based on several principles. One is applying assessments that motivate and inform ability levels. In Outcome-based Education (OBE), assessment is integral to the system. However, e-learning has limitations in providing assessment instruments according to needs, such as assessing complex and detailed aspects and accommodating a variety of numerical and linguistic assessment data. Moreover, the presence and involvement of learners affect their performance and learning outcomes. This study proposes a learner assessment system in e-learning with the OBE approach, including learning design, activity performance analysis, ability level determination, and recommendations. This system adds the e-rubric to e-learning to overcome instrument limitations and accommodate comprehensive assessments. Various numerical and linguistic assessment data are unified using 2-tuple fuzzy linguistics, producing ability levels as two tuples. Performance analysis was based on event log data using descriptive statistical technique and alignment-based conformance checking, from frequency, time, and sequence of activity objects, resulting in five activity performance variables. The performance value of each variable is converted into High, Medium, or Low levels. The ability and performance levels are processed using rule-based methods to produce recommendations for learning stages and activity performance directions. The results of this research can be used as input for academic stakeholders and online learning providers and potentially be applied to the advancement of e-learning in higher education. Doi: 10.28991/HIJ-2024-05-03-03 Full Text: PDF

Advancing predictive maintenance: a deep learning approach to sensor and event-log data fusion

PurposeThis study aims to introduce an innovative approach to predictive maintenance by integrating time-series sensor data with event logs, leveraging the synergistic potential of deep learning models. The primary goal is to enhance the accuracy of equipment failure predictions, thereby minimizing operational downtime.Design/methodology/approachThe methodology uses a dual-model architecture, combining the patch time series transformer (PatchTST) model for analyzing time-series sensor data and bidirectional encoder representations from transformers for processing textual event log data. Two distinct fusion strategies, namely, early and late fusion, are explored to integrate these data sources effectively. The early fusion approach merges data at the initial stages of processing, while late fusion combines model outputs toward the end. This research conducts thorough experiments using real-world data from wind turbines to validate the approach.FindingsThe results demonstrate a significant improvement in fault prediction accuracy, with early fusion strategies outperforming traditional methods by 2.6% to 16.9%. Late fusion strategies, while more stable, underscore the benefit of integrating diverse data types for predictive maintenance. The study provides empirical evidence of the superiority of the fusion-based methodology over singular data source approaches.Originality/valueThis research is distinguished by its novel fusion-based approach to predictive maintenance, marking a departure from conventional single-source data analysis methods. By incorporating both time-series sensor data and textual event logs, the study unveils a comprehensive and effective strategy for fault prediction, paving the way for future advancements in the field.

The development and pilot of a novel mobile application to assess clinician perception of workload and work environment.

Traditional measures of workload such as wRVUs may not be adequate to understand the impact of workload on key outcomes. The objective of this study was to develop a mobile application to assess, in near real time, clinicians' perception of workload and work environment. We developed the GrittyWork™ application (GW App) using the Chokshi and Mann process model for user-centered digital development. Study occured at a single academic medical center with hospitalist clinicians. Measures included the System Usability Scale (SUS), use measures from GW App, electronic health record (EHR) event log data and note counts, and qualitative interviews. From October 28, 2022 to November 3, 2022, six hospitalist clinicians provided feedback on the early prototype of the GW App, and from February 28, 2023 to June 8, 2023, 30 hospitalist clinicians participated in the pilot while on clinical service. All 30 clinicians (100%) participated in the pilot submitting data for a total of 122 shifts. Participants reported working 10 ± 1 h per day (mean ± SD) and were responsible for an average of 11 ± 3 patients per day. The postpilot evaluation of the GW App showed a SUS score of 86 ± 11 and a participant preference toward mobile application-based surveys (73% of participants). Regarding workload measures, EHR event log data and notes data correlated with physician-reported workloads. Applying user-centered design techniques, we successfully developed a mobile application with high usability. These data can be paired with EHR event log data and outcomes to provide insights into the impact of workloads and work environments on outcomes.

SynBPS: a parametric simulation framework for the generation of event-log data

In the pursuit of ecological validity, current business process simulation methods are calibrated to data from existing processes. This is important for realistic what-if analysis in the context of these processes. However, this is not always the “right tool for the job.” To test hypotheses in the area of predictive process monitoring, it can be more helpful to simulate event-log data from a theoretical process, where all aspects can be manipulated. One example is when assessing the influence of process complexity or variability on the performance of a new prediction method. In this case, the ability to include control variables and systematically change process characteristics is a key to fully understanding their influence. Calibrating a simulation model from observed data alone can in these cases be limiting. This paper proposes a simulation framework, Synthetic Business Process Simulation (SynBPS), a Python library for the generation of event-log data from synthetic processes. Aspects such as process complexity, stability, trace distribution, duration distribution, and case arrivals can be fully controlled by the user. The overall architecture is described in detail, and a demonstration of the framework is presented.

Explaining Variability in Electronic Health Record Effort in Primary Care Ambulatory Encounters.

Electronic health record (EHR) user interface event logs are fast providing another perspective on the value and efficiency EHR technology brings to health care. Analysis of these detailed usage data has demonstrated their potential to identify EHR and clinical process design factors related to user efficiency, satisfaction, and burnout. This study aimed to analyze the event log data across 26 different health systems to determine the variability of use of a single vendor's EHR based on four event log metrics, at the individual, practice group, and health system levels. We obtained de-identified event log data recorded from June 1, 2018, to May 31, 2019, from 26 health systems' primary care physicians. We estimated the variability in total Active EHR Time, Documentation Time, Chart Review Time, and Ordering Time across health systems, practice groups, and individual physicians. In total, 5,444 physicians (Family Medicine: 3,042 and Internal Medicine: 2,422) provided care in a total of 2,285 different practices nested in 26 health systems. Health systems explain 1.29, 3.55, 3.45, and 3.30% of the total variability in Active Time, Documentation Time, Chart Review Time, and Ordering Time, respectively. Practice-level variability was estimated to be 7.96, 13.52, 8.39, and 5.57%, respectively, and individual physicians explained the largest proportion of the variability for those same outcomes 17.09, 27.49, 17.51, and 19.75%, respectively. The most variable physician EHR usage patterns occurs at the individual physician level and decreases as you move up to the practice and health system levels. This suggests that interventions to improve individual users' EHR usage efficiency may have the most potential impact compared with those directed at health system or practice levels.

Lift Engineering Equipment Event Log Data Analysis

This paper is devoted to the collection and analysis of log files of lift engineering equipment, considering their technical complexity and significant impact on the safety of their use. The usage of log analysis can quickly identify and resolve problems, keeping elevators functional, extending service life, and increasing passenger safety. Using a log file of a real IoT monitoring system, the study provides baseline statistics, revealing information about event distribution and hardware status. The analysis focuses on the "Elevator Power On/Off" event type, focusing on the average time for the equipment to transition from an emergency state to a normal state, as well as calculating the probability of remaining in an operating state for 1, 12, or 24 hours. The results highlight the different transition times between the equipment under consideration, providing valuable information for maintenance planning and resource optimization

Role of Theory in Analysing the dynamic of Self-regulated Learning process based on students’ event logs data: A scoping review

Students' capability to metacognitively regulate themselves—cognitively or behaviourally—in their learning plays a pivotal role in determining their academic performance. The dynamic aspect of Self-Regulated Learning (SRL) will be challenging to capture if it relies on perceptual data obtained from students through questionnaires and interviews and only at specific points in time. One potential alternative is to use an approach that captures student activity data in real-time throughout the learning period. Given the context-sensitivity involved in measuring SRL via event data, a solid theoretical foundation is essential in analysing patterns of SRL behaviour using event log data. This scoping review paper aims to identify and map how the empirical studies in this area consider SRL theory or models, not only when interpreting analytical results but also when designing instruction and interpreting SRL indicators from raw data. A thorough literature search was performed on various online databases, including Scopus, IEEExplorer, ProQuest, and Web of Science, to identify relevant studies. Following the PRISMA scoping review (PRISMA-ScR) as a protocol for the review, 39 studies published between 2012 and 2023 were included. This study found limited studies incorporating SRL theory in every analysis stage, from designing instruction to preprocessing event data and interpreting analytical models. This study also highlighted the importance of including contextual and theoretical factors when assessing self-regulatory behavioural patterns.

Detecting abnormal behavior of automatic test equipment using autoencoder with event log data

The utilization of automatic test equipment (ATE) in a wafer test is crucial because it helps maintain optimal temperatures and simultaneously provides electrical power for multiple chips. Ensuring the proper functioning of ATE is essential to avoid productivity losses, such as extended wafer testing times and potential malfunctions. Although previous research has investigated the detection of ATE malfunctions using yields, probe cards, and electrical characteristic data, these approaches exhibit limitations in data characteristics and are not suitable for continuous monitoring. This study introduces a novel approach for detecting abnormal behavior within ATE by using event log data and an autoencoder. Autoencoders have demonstrated efficacy in identifying abnormal behavior, making them apt for detecting such behavior within ATE using event log data. To develop this approach, data analysts and wafer test engineers collaborated to establish a practical methodology that encapsulates the operational characteristics of ATE. This methodology comprises four distinct steps. The proposed approach was assessed using simulated event log data and was proven effective in detecting abnormal behavior within ATE. Furthermore, the approach was applied to real-world ATE data from a semiconductor company, identifying abnormal temperature control and monitoring behavior. This detection can lead to reduced wafer test times and contribute to environmental protection efforts.

Remaining cycle time prediction: Temporal loss functions and prediction consistency

The usefulness of remaining cycle time models for predictive and prescriptive process monitoring depends not only on the overall accuracy of the predictions but also on their earliness: predictions should be as accurate as possible, as early as possible. To give this criterion more weight in model fitting, the paper evaluates three L1 loss functions with temporal decay. All have the property of increasing the weight of residuals from the early stages of a process relative to residuals from later stages but do so to different degrees. The loss functions are used in LSTM networks for training remaining throughout time models of four different business processes based on publicly available event log data sets. Compared to models trained with unweighted L1 loss, the suggested modifications yield small but significant improvements in earliness on out-of-sample data. Neither the unweighted L1 loss nor the modifications led to models with strictly monotonically decreasing predictions of the remaining time.

Automatically reconciling the trade-off between prediction accuracy and earliness in prescriptive business process monitoring

Prescriptive business process monitoring provides decision support to process managers on when and how to adapt an ongoing business process to prevent or mitigate an undesired process outcome. We focus on the problem of automatically reconciling the trade-off between prediction accuracy and prediction earliness in determining when to adapt. Adaptations should happen sufficiently early to provide enough lead time for the adaptation to become effective. However, earlier predictions are typically less accurate than later predictions. This means that acting on less accurate predictions may lead to unnecessary adaptations or missed adaptations.Different approaches were presented in the literature to reconcile the trade-off between prediction accuracy and earliness. So far, these approaches were compared with different baselines, and evaluated using different data sets or even confidential data sets. This limits the comparability and replicability of the approaches and makes it difficult to choose a concrete approach in practice.We perform a comparative evaluation of the main alternative approaches for reconciling the trade-off between prediction accuracy and earliness. Using four public real-world event log data sets and two types of prediction models, we assess and compare the cost savings of these approaches. The experimental results indicate which criteria affect the effectiveness of an approach and help us state initial recommendations for the selection of a concrete approach in practice.

Generating multi-level explanations for process outcome predictions

Process mining focuses on the analysis of event log data to build various process analytical capabilities. Predictive process analytics has emerged as one of such key capabilities and it uses machine learning techniques to construct process prediction models. In recent years, deep neural networks have gained increasing interest in process prediction since they can handle multi-dimensional sequential inputs with minimal information loss. However, they are considered black-box models and existing studies in explaining deep neural network-based process predictions rely on only event-level features for explanation. In this paper, we propose a new approach for generating explanations for process outcome predictions at multiple levels. The approach is underpinned by three different prediction models: a transparent model for generating global explanations based on case-level features, an attention-based deep neural network for generating local explanations based on event-level features, and a novel eXplainable Dual-learning Deep network (XD2-net) for generating local explanations based on case-level features. Using three publicly available datasets, we have tested the applicability of the approach and further examined the multi-level explanations generated by the approach through an elaborate case study. Unlike others, the design of our approach promotes the idea of leveraging the complementary capabilities of different models and utilizing their strengths, rather than focusing on model performance competition. This will contribute towards generating more comprehensive explanations that meet the needs of different end users and purposes in the future.

Event Log Data Quality Issues and Solutions

Process mining is a discipline that analyzes real event data extracted from information systems that support a business process to construct as-is process models and detect performance issues. Process event data are transformed into event logs, where the level of data quality directly impacts the reliability, validity, and usefulness of the derived process insights. The literature offers a taxonomy of preprocessing techniques and papers reporting on solutions for data quality issues in particular scenarios without exploring the relationship between the data quality issues and solutions. This research aims to discover how process mining researchers and practitioners solve certain data quality issues in practice and investigates the nature of the relationship between data quality issues and preprocessing techniques. Therefore, a study was undertaken among prominent process mining researchers and practitioners, gathering information regarding the perceived importance and frequency of data quality issues and solutions and the participants’ recommendations on preprocessing technique selection. The results reveal the most important and frequent data quality issues and preprocessing techniques and the gap between their perceived frequency and importance. Consequently, an overview of how researchers and practitioners solve data quality issues is presented, allowing the development of recommendations.

Process Mining for resilient airport operations: A case study of Munich Airport’s turnaround process

The aviation industry has faced significant challenges in recent years, including a punctuality crisis in 2018/19 and the ongoing impact of COVID-19 on operations since March 2020. Hence, the industry seeks innovative ways to optimize its operations and become more resilient. Process Mining (PM) has proven valuable in various settings and industries by analyzing event-log data extracted from existing information systems. As airports play a critical role within the aviation network, we expand on a case study-based methodology and employ PM techniques to analyze data generated within the airport collaborative decision-making (ACDM) framework for the turnaround process at Munich Airport. We show that applying PM enhances operational transparency, reveals performance differences between ground-handling corporations and airlines, and identifies data quality problems with the implemented ACDM framework. Additionally, we develop a conceptual framework demonstrating the positive influence of PM on airport resilience. Our study contributes to the aviation literature and resilience theory by showcasing the potential of PM for analyzing and optimizing operational airport processes. Since the ACDM framework is widely used in Europe, researchers and practitioners can apply our approach to improve turnaround processes at other European airports.

A hybrid feature learning approach based on convolutional kernels for ATM fault prediction using event-log data

Predictive Maintenance (PdM) methods aim to facilitate the scheduling of maintenance work before equipment failure. In this context, detecting early faults in automated teller machines (ATMs) has become increasingly important since these machines are susceptible to various types of unpredictable failures. ATMs track execution status by generating massive event-log data that collect system messages unrelated to the failure event. Predicting machine failure based on event logs poses additional challenges, mainly in extracting features that might represent sequences of events indicating impending failures. Accordingly, feature learning approaches are currently being used in PdM, where informative features are learned automatically from minimally processed sensor data. However, a gap remains to be seen on how these approaches can be exploited for deriving relevant features from event-log-based data. To fill this gap, we present a predictive model based on a convolutional kernel (MiniROCKET and HYDRA) to extract features from the original event-log data and a linear classifier to classify the sample based on the learned features. The proposed methodology is applied to a significant real-world collected dataset. Experimental results demonstrated how one of the proposed convolutional kernels (i.e. HYDRA) exhibited the best classification performance (accuracy of 0.759 and AUC of 0.693). In addition, statistical analysis revealed that the HYDRA and MiniROCKET models significantly overcome one of the established state-of-the-art approaches in time series classification (InceptionTime), and three non-temporal ML methods from the literature. The predictive model was integrated into a container-based decision support system to support operators in the timely maintenance of ATMs.

Optimizing Decision Making on Business Processes Using a Combination of Process Mining, Job Shop, and Multivariate Resource Clustering

The current business environment has no room for inefficiency as it can cause companies to lose out to their competitors, to lose customer trust, and to experience cost overruns. Business processes within the company continue to grow and cause them to run more complex. The large scale and complexity of business processes pose a challenge in improving the quality of process model because the effectiveness of time and the efficiency of existing resources are the biggest challenges. In the context of optimizing business processes with a process mining approach, most current process models are optimized with a trace clustering approach to explore the model and to perform analysis on the resulting process model. Meanwhile, in the event log data, not only the activities but also the other resources, such as records of employee or staff working time, process service time, and processing costs, are recorded. This article proposes a mechanism alternative to optimize business processes by exploring the resources that occur in the process. The mechanism is carried out in three stages. The first stage is optimizing the job shop scheduling method from the generated event log. Scheduling the time becomes a problem in the job shop. Utilizing the right time can increase the effectiveness of performance in order to reduce costs. Scheduling can be defined as the allocation of multiple jobs in a series of machines, in which each machine only does one job at a time. In general, scheduling becomes a problem when sequencing the operations and allocating them into specific time slots without prolonging the technical and capacity constraints. The second stage is generating the resource value that is recorded in the event log from the results of analysis of the previous stage, namely, job shop scheduling. The resource values are multivariate and then clustered to determine homogeneous clusters. The last stage is optimizing the nonlinear multipolynomials in the homogeneous cluster formed by using the Hessian solution. The results obtained are analyzed to get recommendations on business processes that are appropriate for the company’s needs. The impact of long waiting times will increase service costs, but by improving workload, costs can be reduced. The process model and the value of service costs resulting from the mechanism in the research can be a reference for process owners in evaluating and improving ongoing processes.

GCN-ONLSTM: Process Next Event Prediction Method based on Spatio-Temporal Feature Fusion

The task of predicting the next event of a process is the focus of research in the field of predictive process monitoring. Most of the existing methods to achieve this task only process the event log trace as a one-dimensional sequence or regular two-dimensional image data, without considering the simultaneous internal synchronization of the event log. It contains temporal and spatial feature information. In addition, existing studiesignore that trace data is a non-Euclidean structure with topological relationships. In order to solve the above problems and further improve the accuracy of model prediction, this paper constructs a GCN-ONLSTM network that fuses temporal and spatial dimension features, constructs the spatial relationship between events through a two-layer graph convolution network, and improves the feature expression ability of data. And combined with ONLSTM (Ordered Neurons LSTM) network to process the hierarchical structure of trace sequence, to further solve the long-term dependency problem. The ablation experiments and comparison experiments are carried out through 6 BPI public event log data. The results show that the proposed method has significantly improved prediction accuracy in each event log compared with other existing deep learning methods, and the highest is higher than the traditional LSTM (Long Short-term Memory) increased by 8.63%, it can be considered that this method has better performance for the next event prediction task of the process.

SYSTEM FOR CYBER SECURITY EVENTS CORRELATION AND INCIDENT MANAGEMENT IN CRITICAL INFRASTRUCTURE OBJECTS

Modern information infrastructure consists of a large number of systems and components that require constant monitoring and control. To identify, analyze and eliminate possible cyber threats, it is recommended to use a single common solution - the so-called SIEM systems. SIEM technology collects event log data, detects unusual activity through real-time analysis, identifies threats, generates alerts, and suggests appropriate action scenarios. Today, the number and quality of SIEM systems has grown significantly, and the latest technologies of artificial intelligence, the Internet of Things, and cloud technologies are used to ensure fast and effective detection of threats. Thus, the work carried out a study of modern SIEM systems, their functionality, basic principles of operation, as well as a comparative analysis of their capabilities and differences, advantages and disadvantages of use. In addition, a universal system of event correlation and management of cyber security incidents at critical infrastructure facilities was developed and experimentally investigated. Models of the operation of the hybrid security data storage have been developed, which allow the indexing service to access external data storages, to perform scaling when the volume of data increases, to ensure high search speed, etc. Models, methods and algorithms for the operation of a distributed data bus have been developed, which allow for high speed processing of large flows of information, minimal delays in data processing, high resistance to failures, flexibility and expandability of storage. The proposed system is designed to solve a number of current cyber security problems and meets the main requirements of international standards and best global practices regarding the creation of cyber incident management systems.

Process Discovery and Refinement of an Enterprise Management System

The need for the analysis of modern businesses is rapidly increasing as the supporting enterprise systems generate more and more data. This data can be extremely valuable for executing organizations because the data allows constant monitoring, analyzing, and improving the underlying processes, which leads to the reduction of cost and the improvement of the quality. Process mining is a useful technique for analyzing enterprise systems by using an event log that contains behaviours. This research focuses on the process discovery and refinement using real-life event log data collected from a large multinational organization that deals with coatings and paints. By investigating and analyzing their order handling processes, this study aims at learning a model that gives insight inspection of the processes and performance analysis. Furthermore, the animation is also performed for the better inspection, diagnostics, and compliance-related questions to specify the system. The configuration of the system and the conformance checking for further enhancement is also addressed in this research. To achieve the objectives, this research uses process mining techniques, i.e. process discovery in the form of formal Petri nets models with the help of process maps, and process refinement through conformance checking and enhancement. Initially, the identified executed process is reconstructed by using the process discovery techniques. Following the reconstruction, we perform a deep analysis for the underlying process to ensure the process improvement and redesigning. Finally, some recommendations are made to improve the enterprise management system processes.

Evaluation of patient transport service in hospitals using process mining methods: Patients' perspective.

Designing healthcare facilities and their processes is a complex task which influences the quality and efficiency of healthcare services. The ongoing demand for healthcare services and cost burdens necessitate the application of analytical methods to enhance the overall service efficiency in hospitals. However, the variability in healthcare processes makes it highly complicated to accomplish this aim. This study addresses the complexity in the patient transport service process at a German hospital, and proposes a method based on process mining to obtain a holistic approach to recognise bottlenecks and main reasons for delays and resulting high costs associated with idle resources. To this aim, the event log data from the patient transport software system is collected and processed to discover the sequences and the timeline of the activities for the different cases of the transport process. The comparison between the actual and planned processes from the data set of the year 2020 shows that, for example, around 36% of the cases were 10 or more minutes delayed. To find delay issues in the process flow and their root causes the data traces of certain routes are intensively assessed. Additionally, the compliance with the predefined Key Performance Indicators concerning travel time and delay thresholds for individual cases was investigated. The efficiency of assignment of the transport requests to the transportation staff are also evaluated which gives useful understanding regarding staffing potential improvements. The research shows that process mining is an efficient method to provide comprehensive knowledge through process models that serve as Interactive Process Indicators and to extract significant transport pathways. It also suggests a more efficient patient transport concept and provides the decision makers with useful managerial insights to come up with efficient patient-centred analysis of transportation services through data from supporting information systems.