bluuebunny/crossref_metadata_2025_split huggingface dataset:
LSTM-Based Anomaly Detection of Process Instances: Benchmark and Tweaks
Authors: Lahann Johannes , Pfeiffer Peter , Fettke Peter 
Published: March 2023

AbstractAnomaly detection can identify deviations in event logs and allows businesses to infer inconsistencies, bottlenecks, and optimization opportunities in their business processes. In recent years, various anomaly detection algorithms for business processes have been proposed based on either process discovery or machine learning algorithms. While there are apparent differences between machine learning and process discovery approaches, it is often unclear how they perform in comparison. Furthermore, deep learning research in other domains has shown that advancements did not solely come from improved model architecture but were often due to minor pre-processing and training procedure refinements. For this reason, this paper aims to set up a broad benchmark and establish a baseline for deep learning-based anomaly detection of process instances. To this end, we introduce a simple LSTM-based anomaly detector utilizing a collection of minor refinements and compare it with existing approaches. The results suggest that the proposed method can significantly outperform the existing approaches on a large number of event logs consistently.
Genetic Algorithms for AutoML in Process Predictive Monitoring
Authors: Kwon Nahyun , Comuzzi Marco 
Published: March 2023

AbstractIn recent years, AutoML has emerged as a promising technique for reducing computational and time cost by automating the development of machine learning models. Existing AutoML tools cannot be applied directly to process predictive monitoring (PPM), because they do not support several configuration parameters that are PPM-specific, such as trace bucketing or encoding. In other words, they are only specialized in finding the best configuration of machine learning model hyperparameters. In this paper, we present a simple yet extensible framework for AutoML in PPM. The framework uses genetic algorithms to explore a configuration space containing both PPM-specific parameters and the traditional machine learning model hyperparameters. We design four different types of experiments to verify the effectiveness of the proposed approach, comparing its performance in respect of random search of the configuration space, using two publicly available event logs. The results demonstrate that the proposed approach outperforms consistently the random search.
Outcome-Oriented Predictive Process Monitoring on Positive and Unlabelled Event Logs
Authors: Peeperkorn Jari , Ortega Vázquez Carlos , Stevens Alexander , De Smedt Johannes , vanden Broucke Seppe , De Weerdt Jochen 
Published: March 2023

AbstractA lot of recent literature on outcome-oriented predictive process monitoring focuses on using models from machine and deep learning. In this literature, it is assumed the outcome labels of the historical cases are all known. However, in some cases, the labelling of cases is incomplete or inaccurate. For instance, you might only observe negative customer feedback, fraudulent cases might remain unnoticed. These cases are typically present in the so-called positive and unlabelled (PU) setting, where your data set consists of a couple of positively labelled examples and examples which do not have a positive label, but might still be examples of a positive outcome. In this work, we show, using a selection of event logs from the literature, the negative impact of mislabelling cases as negative, more specifically when using XGBoost and LSTM neural networks. Furthermore, we show promising results on real-life datasets mitigating this effect, by changing the loss function used by a set of models during training to those of unbiased Positive-Unlabelled (uPU) or non-negative Positive-Unlabelled (nnPU) learning.
Enhancing Data-Awareness of Object-Centric Event Logs
Authors: Goossens Alexandre , De Smedt Johannes , Vanthienen Jan , van der Aalst Wil M. P. 
Published: March 2023

AbstractWhen multiple objects are involved in a process, there is an opportunity for processes to be discovered from different angles with new information that previously might not have been analyzed from a single object point of view. This does require that all the information of event/object attributes and their values are stored within logs including attributes that have a list of values or attributes with values that change over time. It also requires that attributes can unambiguously be linked to an object, an event or both. As such, object-centric event logs are an interesting development in process mining as they support the presence of multiple types of objects. First, this paper shows that the current object-centric event log formats do not support the aforementioned aspects to their full potential since the possibility to support dynamic object attributes (attributes with changing values) is not supported by existing formats. Next, this paper introduces a novel enriched object-centric event log format tackling the aforementioned issues alongside an algorithm that automatically translates XES logs to this Data-aware OCEL (DOCEL) format.
Clustering Analysis and Frequent Pattern Mining for Process Profile Analysis: An Exploratory Study for Object-Centric Event Logs
Authors: Faria Junior Elio Ribeiro , Neubauer Thais Rodrigues , Fantinato Marcelo , Peres Sarajane Marques 
Published: March 2023

AbstractObject-centric event log is a format for properly organizing information from different views of a business process into an event log. The novelty in such a format is the association of events with objects, which allows different notions of cases to be analyzed. The addition of new features has brought an increase in complexity. Clustering analysis can ease this complexity by enabling the analysis to be guided by process behaviour profiles. However, identifying which features describe the singularity of each profile is a challenge. In this paper, we present an exploratory study in which we mine frequent patterns on top of clustering analysis as a mechanism for profile characterization. In our study, clustering analysis is applied in a trace clustering fashion over a vector representation for a flattened event log extracted from an object-centric event log, using a unique case notion. Then, frequent patterns are discovered in the event sublogs associated with clusters and organized according to that original object-centric event log. The results obtained in preliminary experiments show association rules reveal more evident behaviours in certain profiles. Despite the process underlying each cluster may contain the same elements (activities and transitions), the behaviour trends show the relationships between such elements are supposed to be different. The observations depicted in our analysis make room to search for subtler knowledge about the business process under scrutiny.
Process Modeling and Conformance Checking in Healthcare: A COVID-19 Case Study
Authors: Benevento Elisabetta , Pegoraro Marco , Antoniazzi Mattia , Beyel Harry H. , Peeva Viki , Balfanz Paul , van der Aalst Wil M. P. , Martin Lukas , Marx Gernot 
Published: March 2023

AbstractThe discipline of process mining has a solid track record of successful applications to the healthcare domain. Within such research space, we conducted a case study related to the Intensive Care Unit (ICU) ward of the Uniklinik Aachen hospital in Germany. The aim of this work is twofold: developing a normative model representing the clinical guidelines for the treatment of COVID-19 patients, and analyzing the adherence of the observed behavior (recorded in the information system of the hospital) to such guidelines. We show that, through conformance checking techniques, it is possible to analyze the care process for COVID-19 patients, highlighting the main deviations from the clinical guidelines. The results provide physicians with useful indications for improving the process and ensuring service quality and patient satisfaction. We share the resulting model as an open-source BPMN file.
Research Paper: Process Mining and Synthetic Health Data: Reflections and Lessons Learnt
Authors: Bullward Alistair , Aljebreen Abdulaziz , Coles Alexander , McInerney Ciarán , Johnson Owen 
Published: March 2023

AbstractAnalysing the treatment pathways in real-world health data can provide valuable insight for clinicians and decision-makers. However, the procedures for acquiring real-world data for research can be restrictive, time-consuming and risks disclosing identifiable information. Synthetic data might enable representative analysis without direct access to sensitive data. In the first part of our paper, we propose an approach for grading synthetic data for process analysis based on its fidelity to relationships found in real-world data. In the second part, we apply our grading approach by assessing cancer patient pathways in a synthetic healthcare dataset (The Simulacrum provided by the English National Cancer Registration and Analysis Service) using process mining. Visualisations of the patient pathways within the synthetic data appear plausible, showing relationships between events confirmed in the underlying non-synthetic data. Data quality issues are also present within the synthetic data which reflect real-world problems and artefacts from the synthetic dataset’s creation. Process mining of synthetic data in healthcare is an emerging field with novel challenges. We conclude that researchers should be aware of the risks when extrapolating results produced from research on synthetic data to real-world scenarios and assess findings with analysts who are able to view the underlying data.
Predicting Patient Care Acuity: An LSTM Approach for Days-to-day Prediction
Authors: Bekelaar Jorg W. R. , Luime Jolanda J. , de Carvalho Renata M. 
Published: March 2023

AbstractIn recent years, hospitals and other care providers in the Netherlands are coping with a widespread nursing shortage and a directly related increase in nursing workload. This nursing shortage combined with the high nursing workload is associated with higher levels of burnout and reduced job satisfaction among nurses. However, not only the nurses, but also the patients are affected as an increasing nursing workload adversely affects patient safety and satisfaction. Therefore, the aim of this research is to predict the care acuity corresponding to an individual patient for the next admission day, by using the available structured hospital data of the previous admission days. For this purpose, we make use of an LSTM model that is able to predict the care acuity of the next day, based on the hospital data of all previous days of an admission. In this paper, we elaborate on the architecture of the LSTM model and we show that the prediction accuracy of the LSTM model increases with the increase of the available amount of historical event data. We also show that the model is able to identify care acuity differences in terms of the amount of support needed by the patient. Moreover, we discuss how the predictions can be used to identify which patient care related characteristics and different types of nursing activities potentially contribute to the care acuity of a patient.
Measuring the Impact of COVID-19 on Hospital Care Pathways
Authors: Puthur Christin , Aljebreen Abdulaziz , McInerney Ciarán , Mebrahtu Teumzghi , Lawton Tom , Johnson Owen 
Published: March 2023

AbstractCare pathways in hospitals around the world reported significant disruption during the recent COVID-19 pandemic but measuring the actual impact is more problematic. Process mining can be useful for hospital management to measure the conformance of real-life care to what might be considered normal operations. In this study, we aim to demonstrate that process mining can be used to investigate process changes associated with complex disruptive events. We studied perturbations to accident and emergency (A &E) and maternity pathways in a UK public hospital during the COVID-19 pandemic. Co-incidentally the hospital had implemented a Command Centre approach for patient-flow management affording an opportunity to study both the planned improvement and the disruption due to the pandemic. Our study proposes and demonstrates a method for measuring and investigating the impact of such planned and unplanned disruptions affecting hospital care pathways. We found that during the pandemic, both A &E and maternity pathways had measurable reductions in the mean length of stay and a measurable drop in the percentage of pathways conforming to normative models. There were no distinctive patterns of monthly mean values of length of stay nor conformance throughout the phases of the installation of the hospital’s new Command Centre approach. Due to a deficit in the available A &E data, the findings for A &E pathways could not be interpreted.
Defining Data Quality Issues in Process Mining with IoT Data
Authors: Bertrand Yannis , Van Belle Rafaël , De Weerdt Jochen , Serral Estefanía 
Published: March 2023

AbstractIoT devices supporting business processes (BPs) in sectors like manufacturing, logistics or healthcare collect data on the execution of the processes. In the last years, there has been a growing awareness of the opportunity to use the data these devices generate for process mining (PM) by deriving an event log from a sensor log via event abstraction techniques. However, IoT data are often affected by data quality issues (e.g., noise, outliers) which, if not addressed at the preprocessing stage, will be amplified by event abstraction and result in quality issues in the event log (e.g., incorrect events), greatly hampering PM results. In this paper, we review the literature on PM with IoT data to find the most frequent data quality issues mentioned in the literature. Based on this, we then derive six patterns of poor sensor data quality that cause event log quality issues and propose solutions to avoid or solve them.
Creating Translucent Event Logs to Improve Process Discovery
Authors: Beyel Harry H. , van der Aalst Wil M. P. 
Published: March 2023

AbstractEvent logs capture information about executed activities. However, they do not capture information about activities that could have been performed, i.e., activities that were enabled during a process. Event logs containing information on enabled activities are called translucent event logs. Although it is possible to extract translucent event logs from a running information system, such logs are rarely stored. To increase the availability of translucent event logs, we propose two techniques. The first technique records the system’s states as snapshots. These snapshots are stored and linked to events. A user labels patterns that describe parts of the system’s state. By matching patterns with snapshots, we can add information about enabled activities. We apply our technique in a small setting to demonstrate its applicability. The second technique uses a process model to add information concerning enabled activities to an existing traditional event log. Data containing enabled activities are valuable for process discovery. Using the information on enabled activities, we can discover more correct models.
Aggregating Event Knowledge Graphs for Task Analysis
Authors: Klijn Eva L. , Mannhardt Felix , Fahland Dirk 
Published: March 2023

AbstractAggregation of event data is a key operation in process mining for revealing behavioral features of processes for analysis. It has primarily been studied over sequences of events in event logs. The data model of event knowledge graphs enables new analysis questions requiring new forms of aggregation. We focus on analyzing task executions in event knowledge graphs. We show that existing aggregation operations are inadequate and propose new aggregation operations, formulated as query operators over labeled property graphs. We show on the BPIC’17 dataset that the new aggregation operations allow gaining new insights into differences in task executions, actor behavior, and work division.
A Combined Approach of Process Mining and Rule-Based AI for Study Planning and Monitoring in Higher Education
Authors: Wagner Miriam , Helal Hayyan , Roepke Rene , Judel Sven , Doveren Jens , Goerzen Sergej , Soudmand Pouya , Lakemeyer Gerhard , Schroeder Ulrik , van der Aalst Wil M. P. 
Published: March 2023

AbstractThis paper presents an approach of using methods of process mining and rule-based artificial intelligence to analyze and understand study paths of students based on campus management system data and study program models. Process mining techniques are used to characterize successful study paths, as well as to detect and visualize deviations from expected plans. These insights are combined with recommendations and requirements of the corresponding study programs extracted from examination regulations. Here, event calculus and answer set programming are used to provide models of the study programs which support planning and conformance checking while providing feedback on possible study plan violations. In its combination, process mining and rule-based artificial intelligence are used to support study planning and monitoring by deriving rules and recommendations for guiding students to more suitable study paths with higher success rates. Two applications will be implemented, one for students and one for study program designers.
Domain-Driven Event Abstraction Framework for Learning Dynamics in MOOCs Sessions
Authors: Hidalgo Luciano , Munoz-Gama Jorge 
Published: March 2023

AbstractIn conjunction with the rapid expansion of Massive Open Online Courses (MOOCs), academic interest has grown in the analysis of MOOC student study sessions. Education researchers have increasingly regarded process mining as a promising tool with which to answer simple questions, including the order in which resources are completed. However, its application to more complex questions about learning dynamics remains a challenge. For example, do MOOC students genuinely study from a resource or merely skim content to understand what will come next? One common practice is to use the resources directly as activities, resulting in spaghetti process models that subsequently undergo filtering. However, this leads to over-simplified and difficult-to-interpret conclusions. Consequently, an event abstraction becomes necessary, whereby low-level events are combined with high-level activities. A wide range of event abstraction techniques has been presented in process mining literature, primarily in relation to data-driven bottom-up strategies, where patterns are discovered from the data and later mapped to education concepts. Accordingly, this paper proposes a domain-driven top-down framework that allows educators who are less familiar with data and process analytics to more easily search for a set of predefined high-level concepts from their own MOOC data. The framework outlined herein has been successfully tested in a Coursera MOOC, with the objective of understanding the in-session behavioral dynamics of learners who successfully complete their respective courses.
Process Mining for Analyzing Open Questions Computer-Aided Examinations
Authors: Bala Saimir , Revoredo Kate , Mendling Jan 
Published: March 2023

AbstractComputer-based education relies on information systems to support teaching and learning processes. These systems store trace data about the interaction of the learners with their different functionalities. Process mining techniques have been used to evaluate these traces and provide insights to instructors on the behavior of students. However, an analysis of students behavior on solving open-questioned examinations combined with the marks they received is still missing. This analysis can support the instructors not only on improving the design of future edition of the course, but also on improving the structure of online and physical evaluations. In this paper, we use process mining techniques to evaluate the behavioral patterns of students solving computer-based open-ended exams and their correlation with the grades. Our results show patterns of behavior associated to the marks received. We discuss how these results may support the instructor on elaborating future open question examinations.
Process Mining on Curriculum-Based Study Data: A Case Study at a German University
Authors: Hobeck Richard , Pufahl Luise , Weber Ingo 
Published: March 2023

AbstractOn their trajectory through educational university systems, students leave a trace of event data. The analysis of that event data with a process lens poses a set of domain-specific challenges that is addressed in the field of Educational Process Mining (EPM). Despite the vast potential for understanding the progress of students and improving the quality of study programs through process mining, a case study based on an established process mining methodology is still missing. In this paper, we address this gap by applying the state-of-the-art process mining project methodology (PM$$^2$$

2

) in an EPM case study with a focus on student trajectory analysis at a German university. We found that process mining can create actionable items to improve the quality of university education. We also point out domain-specific challenges, like handling reoccurring exams (retaken after failing) for future research in EPM. Finally, we observe insights of some value in our case.
TraVaS: Differentially Private Trace Variant Selection for Process Mining
Authors: Rafiei Majid , Wangelik Frederik , van der Aalst Wil M. P. 
Published: March 2023

AbstractIn the area of industrial process mining, privacy-preserving event data publication is becoming increasingly relevant. Consequently, the trade-off between high data utility and quantifiable privacy poses new challenges. State-of-the-art research mainly focuses on differentially private trace variant construction based on prefix expansion methods. However, these algorithms face several practical limitations such as high computational complexity, introducing fake variants, removing frequent variants, and a bounded variant length. In this paper, we introduce a new approach for direct differentially private trace variant release which uses anonymized partition selection strategies to overcome the aforementioned restraints. Experimental results on real-life event data show that our algorithm outperforms state-of-the-art methods in terms of both plain data utility and result utility preservation.
Generalized non-unitary Haagerup-Izumi modular data from 3D S-fold SCFTs
Authors: Gang Dongmin , Kim Dongyeob 
Published: March 2023

Abstract
By applying the recently proposed (3D rank-0 $$ \mathcal{N} $$
N
=4 SCFT)/(non-unitary TQFTs) correspondence to S-fold SCFTs, we construct an exotic class of non-unitary TQFTs labelled by an integer k ≥ 3. The SCFTs are obtained by gauging diagonal SU(2) subgroup of T[SU(2)] theory with Chern-Simons level k. We give the explicit expression for modular data, S and T matrices, of the TQFTs. When k = 4m2 + 4m + 3 with an integer m ≥ 1, the modular data (modulo a decoupled semion) is identical to a non-unitary Haagerup-Izumi modular data. Thus, we give a physical realization of the exotic non-unitary modular data as well as its generalization using an exotic class of SCFTs.