PM4Py-GPU: A High-Performance General-Purpose Library for Process Mining

BackgroundMeasurement of students' self‐regulation skills is an active topic in education research, as effective assessment helps devising support interventions to foster academic achievement. Measures based on event tracing usually require large amounts of data (e.g., MOOCs and large courses), while aptitude measures are often qualitative and need careful interpretation. Precise and interpretable evaluation of self‐regulation skills in a normal K‐12 classroom thus poses a challenge.ObjectivesThe present study proposes and explores a learning analytics method of combining aptitude and event measures to evaluate student's self‐regulation skills.MethodsAn explorative learning analytics study was conducted in a junior high school mathematics class ( students), using a three‐lesson intervention with digital learning materials. Students first assessed their self‐regulation skills with a self‐report questionnaire, after which trace logs and observations of student behaviour were collected. Learning sessions were extracted from trace logs, clustered, and linked to learning strategies. Students were clustered by the self‐report results and learning behaviour profiles. Session clusters, student behaviour clusters and assignment grades were also tested for association.Results and ConclusionsThe detected session and student behaviour types were linked to learning tactics and strategies found in prior studies. Additionally, association was found between self‐reported self‐regulation skills and the student behaviour obtained from trace logs.ImplicationsThe results demonstrate the feasibility of concurrently using aptitude and event measures on a classroom scale, providing teachers with a tool to evaluate and support self‐regulated learning. Combined with further measures like predictive learning analytics, teachers can obtain an early and highly interpretable picture of at‐risk students in their classes.

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“…Data analysis was completed using Pandas (Reback et al, 2021), PM4Py (Berti et al, 2019) and scikit-learn (Pedregosa et al, 2011).…”

Section: Discussionmentioning

confidence: 99%

Measuring self‐regulated learning in a junior high school mathematics classroom: Combining aptitude and event measures in digital learning materials

Zhidkikh

Saarela

Kärkkäinen

2023

Computer Assisted Learning

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“…For example, for the question “How did you seek treatment?”, we merged responses “Visiting government health facility” and “Visiting private health facility” into “Visiting health facility”. We employed the Heuristic Miner using the PM4Py package in Python (35). The process is visually described in Figure 3.…”

Section: Methodsmentioning

confidence: 99%

Treatment seeking behaviours, antibiotic use and relationships to multi-drug resistance: A study of urinary tract infection patients in Kenya, Tanzania and Uganda

Sado

Keenan

Manataki

et al. 2023

Preprint

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Introduction: Antibacterial resistance (ABR) is a major public health threat. An important accelerating factor is treatment-seeking behaviours, including inappropriate antibiotic (AB) use. In many low- and middle-income countries (LMICs) this includes taking ABs with and without prescription sourced from various providers, including health facilities and community drug sellers. However, investigations of complex treatment-seeking, AB use and drug resistance in LMICs are scarce. Methods: The Holistic Approach to Unravel Antibacterial Resistance in East Africa (HATUA) Consortium collected questionnaire and microbiological data from 6,827 adult outpatients with urinary tract infection (UTI)-like symptoms presenting at healthcare facilities in Kenya, Tanzania and Uganda. Among 6,388 patients we analysed patterns of self-reported treatment seeking behaviours (patient pathways) using process mining and single-channel sequence analysis. Of those with microbiologically confirmed UTI (n=1,946), we used logistic regression to assessed the relationship between treatment seeking behaviour, AB use, and likelihood of having a multi-drug resistant (MDR) UTI. Results: The most common treatment pathways for UTI-like symptoms included attending health facilities, rather than other providers (e.g. drug sellers). Patients from the sites sampled in Tanzania and Uganda, where prevalence of MDR UTI was over 50%, were more likely to report treatment failures, and have repeated visits to clinics/other providers, than those from Kenyan sites, where MDR UTI rates were lower (33%). There was no strong or consistent relationship between individual AB use and risk of MDR UTI, after accounting for country context. Conclusion: The results highlight challenges East African patients face in accessing effective UTI treatment. These challenges increase where rates of MDR UTI are higher, suggesting a reinforcing circle of failed treatment attempts and sustained selection for drug resistance. Whilst individual behaviours may contribute to the risk of MDR UTI, our data show that factors related to context are stronger drivers of ABR.

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“…All implementations were carried out in Python utilizing the available source codes of DAE, BINet, AE, VAE, and LAE provided by their respective authors. The sampling method was implemented according to the original paper's description using the Process Mining for Python (PM4Py) library [56]. The SA-BLSTM-VAE model was trained for 100 epochs.…”

Section: B Implementation Setupmentioning

confidence: 99%

Business Process Anomaly Detection and Root Cause Analysis Using BLSTM-VAE With Attention

El-Aziz,

Fathalla,

Ismail

et al. 2024

IEEE Access

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Detecting anomalous executions in business process data is crucial for safeguarding the efficiency and success of an organization. Unsupervised approaches are commonly used for business process anomaly detection because of the scarcity of labeled anomaly data. However, these approaches often encounter a notable decline in performance because they lack prior knowledge about the anomalies. Additionally, most of them do not perform root cause analysis on the detected anomalies. This study proposes a variational autoencoder-based approach to overcome the performance limitations of existing unsupervised methods and determine the root causes of the detected anomalies. The learning of the variational autoencoder from unlabeled business process data is enhanced in the proposed approach by leveraging different architectural components, namely, the entity embedding technique, the bidirectional long short-term memory network, and the self-attention mechanism. Combining these architectural components in the variational autoencoder architecture leads to learning high-level representations from the business process data and thus improving the reconstruction capability of the variational autoencoder. Furthermore, this study suggests feeding the reconstruction error provided by the variational autoencoder into the logistic regression classifier to improve the accuracy of anomaly detection. The performance of the proposed model was evaluated on real-life and synthetic datasets. The experimental findings indicate that the proposed model outperforms six existing anomaly detection models in terms of precision, recall, and F1-score metrics.

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PM4Py-GPU: A High-Performance General-Purpose Library for Process Mining

Cited by 7 publications

References 6 publications

Measuring self‐regulated learning in a junior high school mathematics classroom: Combining aptitude and event measures in digital learning materials

Measuring self‐regulated learning in a junior high school mathematics classroom: Combining aptitude and event measures in digital learning materials

Treatment seeking behaviours, antibiotic use and relationships to multi-drug resistance: A study of urinary tract infection patients in Kenya, Tanzania and Uganda

Business Process Anomaly Detection and Root Cause Analysis Using BLSTM-VAE With Attention

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