Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction | Paper Citation Record

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This paper cites Process Mining: Data Science in Action.

Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction Process Mining: Data Science in Action

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This paper cites Object-Centric Process Mining: Unrav- eling the Fabric of Real Processes.

Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction Object-Centric Process Mining: Unrav- eling the Fabric of Real Processes

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This paper cites Process Mining Handbook.

Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction Process Mining Handbook

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Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction Principal component analysis

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This paper cites An Experimental Evaluation of Process Concept Drift Detection.

Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction An Experimental Evaluation of Process Concept Drift Detection

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This paper cites Explaining anomalies detected by autoencoders using Shapley Additive Explana- tions.

Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction Explaining anomalies detected by autoencoders using Shapley Additive Explana- tions

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This paper cites The biggest busi- ness process management problems to solve before we die.

Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction The biggest busi- ness process management problems to solve before we die

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This paper cites Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets.

Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets

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This paper cites Process Modeling and Conformance Checking in Healthcare: A COVID-19 Case Study, in: Montali, M., Senderovich, A., Weidlich, M.

Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction Process Modeling and Conformance Checking in Healthcare: A COVID-19 Case Study, in: Montali, M., Senderovich, A., Weidlich, M

Reference 10

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This paper cites Anomaly Detection Using Process Mining, in: Halpin, T., Krogstie, J., Nurcan, S., Proper, E., Schmidt, R., Soffer, P., Ukor, R.

Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction Anomaly Detection Using Process Mining, in: Halpin, T., Krogstie, J., Nurcan, S., Proper, E., Schmidt, R., Soffer, P., Ukor, R

Reference 11

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This paper cites An Alignment Cost-Based Classification of Log Traces Using Machine-Learning, in: Leemans, S., Leopold, H.

Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction An Alignment Cost-Based Classification of Log Traces Using Machine-Learning, in: Leemans, S., Leopold, H

Reference 12

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This paper cites Robust PCA via Principal Compo- nent Pursuit: A review for a comparative evaluation in video surveillance.

Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction Robust PCA via Principal Compo- nent Pursuit: A review for a comparative evaluation in video surveillance

Reference 13

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This paper cites Anomaly Detection: A Sur- vey.

Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction Anomaly Detection: A Sur- vey

Reference 14

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This paper cites PMiner: Process Mining using Deep Autoencoder for Anomaly Detection and Re- construction of Business Processes.

Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction PMiner: Process Mining using Deep Autoencoder for Anomaly Detection and Re- construction of Business Processes

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Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction Unresolved cited work

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Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction Unresolved cited work

Reference 17

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This paper cites Deep reinforcement learn- ing for data-efficient weakly supervised business process anomaly detec- tion.

Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction Deep reinforcement learn- ing for data-efficient weakly supervised business process anomaly detec- tion

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This paper cites GRASPED: A GRU- AE Network Based Multi-Perspective Business Process Anomaly Detec- tion Model.

Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction GRASPED: A GRU- AE Network Based Multi-Perspective Business Process Anomaly Detec- tion Model

Reference 19

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This paper cites W AKE: A Weakly Supervised Business Process Anomaly Detection Framework via a Pre- Trained Autoencoder.

Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction W AKE: A Weakly Supervised Business Process Anomaly Detection Framework via a Pre- Trained Autoencoder

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This paper cites Weakly Supervised Anomaly Detection: A Survey.

Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction Weakly Supervised Anomaly Detection: A Survey

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This paper cites Event log anomaly detection method based on auto-encoder and control flow.

Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction Event log anomaly detection method based on auto-encoder and control flow

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This paper cites Auto-Encoding Variational Bayes.

Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction Auto-Encoding Variational Bayes

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This paper cites Detecting anomalies in business process event logs using statistical leverage.

Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction Detecting anomalies in business process event logs using statistical leverage

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This paper cites A Systematic Review of Anomaly Detec- tion for Business Process Event Logs.

Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction A Systematic Review of Anomaly Detec- tion for Business Process Event Logs

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Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction Unresolved cited work

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This paper cites LSTM-Based Anomaly Detection of Process Instances: Benchmark and Tweaks, in: Mon- tali, M., Senderovich, A., Weidlich, M.

Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction LSTM-Based Anomaly Detection of Process Instances: Benchmark and Tweaks, in: Mon- tali, M., Senderovich, A., Weidlich, M

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This paper cites A Framework for Detecting Devi- ations in Complex Event Logs.

Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction A Framework for Detecting Devi- ations in Complex Event Logs

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Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction Algorithms for anomaly detec- tion of traces in logs of process aware information systems

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Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction Unresolved cited work

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This paper cites A unified approach to interpreting model predictions, in: Proceedings of the 31st International Confer- ence on Neural Information Processing Systems, p.

Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction A unified approach to interpreting model predictions, in: Proceedings of the 31st International Confer- ence on Neural Information Processing Systems, p

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This paper cites Opportunities and Challenges for Process Mining in Orga- nizations: Results of a Delphi Study.

Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction Opportunities and Challenges for Process Mining in Orga- nizations: Results of a Delphi Study

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This paper cites A comparison of proximity- based methods for detecting temporal anomalies in business processes.

Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction A comparison of proximity- based methods for detecting temporal anomalies in business processes

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This paper cites Anomaly detection for industrial control systems using process mining.

Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction Anomaly detection for industrial control systems using process mining

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Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction Fault detection based on Kernel Principal Component Analysis

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This paper cites Analyzing business process anomalies using autoencoders.

Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction Analyzing business process anomalies using autoencoders

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This paper cites BINet: Multi- perspective business process anomaly classification.

Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction BINet: Multi- perspective business process anomaly classification

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This paper cites DeepAlign: Alignment-Based Process Anomaly Correction Using Recurrent Neu- ral Networks, in: Advanced Information Systems Engineering, Springer International Publishing, Cham.

Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction DeepAlign: Alignment-Based Process Anomaly Correction Using Recurrent Neu- ral Networks, in: Advanced Information Systems Engineering, Springer International Publishing, Cham

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Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction Discovering pro- cess models for the analysis of application failures under uncertainty of event logs

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This paper cites Analyzing medical data with process min- ing: A COVID-19 case study, in: Abramowicz, W., Auer, S., Str ´ozyna, M.

Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction Analyzing medical data with process min- ing: A COVID-19 case study, in: Abramowicz, W., Auer, S., Str ´ozyna, M

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This paper cites Recent Advances in Trustworthy Explainable Artificial Intelligence: Sta- tus, Challenges, and Perspectives.

Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction Recent Advances in Trustworthy Explainable Artificial Intelligence: Sta- tus, Challenges, and Perspectives

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Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction Unresolved cited work

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This paper cites Anomaly detection in busi- ness processes using process mining and fuzzy association rule learning.

Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction Anomaly detection in busi- ness processes using process mining and fuzzy association rule learning

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This paper cites Using log analytics and process mining to enable self-healing in the Internet of Things.

Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction Using log analytics and process mining to enable self-healing in the Internet of Things

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This paper cites Shapley Values of Reconstruction Errors of PCA for Explaining Anomaly Detection, in: 2019 International Conference on Data Mining Workshops (ICDMW), pp.

Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction Shapley Values of Reconstruction Errors of PCA for Explaining Anomaly Detection, in: 2019 International Conference on Data Mining Workshops (ICDMW), pp

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This paper cites Process Mining Encoding via Meta-learning for an Enhanced Anomaly Detection, in: New Trends in Database and Information Systems, pp.

Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction Process Mining Encoding via Meta-learning for an Enhanced Anomaly Detection, in: New Trends in Database and Information Systems, pp

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This paper cites Trace encoding in process mining: A survey and benchmarking.

Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction Trace encoding in process mining: A survey and benchmarking

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This paper cites Linear dis- criminant analysis: A detailed tutorial.

Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction Linear dis- criminant analysis: A detailed tutorial

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This paper cites Unsupervised Learning Meth- ods for Anomaly Detection and Log Quality Improvement Using Process Event Log.

Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction Unsupervised Learning Meth- ods for Anomaly Detection and Log Quality Improvement Using Process Event Log

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Source-reported events for the cited work

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Observation 2701515c-205d-453b-96fd-546599857c5d · outbound

This paper cites an unresolved cited work.

Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction Unresolved cited work

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Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction Recompose Event Sequences vs

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Resolution: malformed identifier; arxiv_id_nonexistent, observed 2026-08-07T19:01:49.694865Z

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This paper cites Fraud Detection under Multi-Sourced Extremely Noisy Annotations, in: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, p.

Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction Fraud Detection under Multi-Sourced Extremely Noisy Annotations, in: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, p

Reference 53

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This paper cites ADMoE: Anomaly Detection with Mixture-of-Experts from Noisy La- bels, in: Proceedings of the AAAI Conference on Artificial Intelli- gence.

Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction ADMoE: Anomaly Detection with Mixture-of-Experts from Noisy La- bels, in: Proceedings of the AAAI Conference on Artificial Intelli- gence

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Resolution: verified exact; doi, observed 2026-08-07T19:01:49.174639Z

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Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction Feature Encoding With Autoencoders for Weakly Supervised Anomaly Detection

Reference 55

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Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction Sparse Principal Component Analysis

Reference 56

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Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction Unresolved cited work

Reference 190

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Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction Unresolved cited work

Reference 2022

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This paper cites IEEE Transac- tions on Industrial Informatics , 1–11doi:10.1109/TII.2023.3246983.

Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction IEEE Transac- tions on Industrial Informatics , 1–11doi:10.1109/TII.2023.3246983

Reference 2023

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-07T19:01:51.933379Z

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This paper cites an unresolved cited work.

Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction Unresolved cited work

Reference 2465

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-07T19:01:49.506036Z

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