Jari Peeperkorn

Orcid: 0000-0003-4644-4881

According to our database1, Jari Peeperkorn authored at least 15 papers between 2020 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Bibliography

2024
Validation set sampling strategies for predictive process monitoring.
Inf. Syst., March, 2024

Towards a Simple and Extensible Standard for Object-Centric Event Data (OCED) - Core Model, Design Space, and Lessons Learned.
CoRR, 2024

Dynamic and Scalable Data Preparation for Object-Centric Process Mining.
CoRR, 2024

2023
Can recurrent neural networks learn process model structure?
J. Intell. Inf. Syst., August, 2023

Global conformance checking measures using shallow representation and deep learning.
Eng. Appl. Artif. Intell., 2023

Manifold Learning for Adversarial Robustness in Predictive Process Monitoring.
Proceedings of the 5th International Conference on Process Mining, 2023

Vector Representation for Business Process: Graph Embedding for Domain Knowledge Integration.
Proceedings of the International Conference on Machine Learning and Applications, 2023

Discovering high-quality process models despite data scarcity.
Proceedings of the Companion Proceedings of the 42nd International Conference on Conceptual Modeling: ER Forum, 2023

2022
Assessing the Robustness in Predictive Process Monitoring through Adversarial Attacks.
Proceedings of the 4th International Conference on Process Mining, 2022

Outcome-Oriented Predictive Process Monitoring on Positive and Unlabelled Event Logs.
Proceedings of the Process Mining Workshops, 2022

Enhancing Stochastic Petri Net-based Remaining Time Prediction using k-Nearest Neighbors.
Proceedings of the Workshop on Algorithms & Theories for the Analysis of Event Data co-located with the 43rd International Conference on Application and Theory of Petri Nets and Concurrency (Petri Nets 2022), 2022

2021
Quantifying Explainability in Outcome-Oriented Predictive Process Monitoring.
Proceedings of the Process Mining Workshops - ICPM 2021 International Workshops, Eindhoven, The Netherlands, October 31, 2021

Can Deep Neural Networks Learn Process Model Structure? An Assessment Framework and Analysis.
Proceedings of the Process Mining Workshops - ICPM 2021 International Workshops, Eindhoven, The Netherlands, October 31, 2021

2020
Supervised Conformance Checking Using Recurrent Neural Network Classifiers.
Proceedings of the Process Mining Workshops, 2020

Conformance Checking Using Activity and Trace Embeddings.
Proceedings of the Business Process Management Forum, 2020


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