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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Conformance Checking Approximation Using Simulation
Mohammadreza Fani Sani, Juan J. Garza Gonzalez, Sebastiaan J. van Zelst, et al.
(2020), pp. 105-112
Closed Access | Times Cited: 20

Showing 20 citing articles:

Conformance Checking: Foundations, Milestones and Challenges
Josep Carmona, Boudewijn F. van Dongen, Matthias Weidlich
Lecture notes in business information processing (2022), pp. 155-190
Open Access | Times Cited: 30
Static and dynamic techniques for iterative test-driven modelling of Dynamic Condition Response Graphs
Axel Kjeld Fjelrad Christfort, Vlad Paul Cosma, Søren Debois, et al.
Data & Knowledge Engineering (2025), pp. 102413-102413
Closed Access
Model-Independent Error Bound Estimation for Conformance Checking Approximation
Mohammadreza Fani Sani, Martin Kabierski, Sebastiaan J. van Zelst, et al.
Lecture notes in business information processing (2024), pp. 369-382
Closed Access | Times Cited: 3
Discovering generative models from event logs: data-driven simulation vs deep learning
Manuel Camargo, Marlon Dumas, Oscar González-Rojas
PeerJ Computer Science (2021) Vol. 7, pp. e577-e577
Open Access | Times Cited: 21
Global conformance checking measures using shallow representation and deep learning
Jari Peeperkorn, Seppe vanden Broucke, Jochen De Weerdt
Engineering Applications of Artificial Intelligence (2023) Vol. 123, pp. 106393-106393
Closed Access | Times Cited: 5
Interactive Process Improvement Using Simulation of Enriched Process Trees
Mahsa Pourbafrani, Wil M. P. van der Aalst
Lecture notes in computer science (2022), pp. 61-76
Open Access | Times Cited: 6
Efficient Approximate Conformance Checking Using Trie Data Structures
Ahmed Awad, Kristo Raun, Matthias Weidlich
(2021), pp. 1-8
Closed Access | Times Cited: 8
Sampling What Matters: Relevance-guided Sampling of Event Logs
Martin Kabierski, Hoang Lam Nguyen, Lars Grunske, et al.
(2021), pp. 64-71
Closed Access | Times Cited: 6
Efficient Optimal Alignment Between Dynamic Condition Response Graphs and Traces
Axel Kjeld Fjelrad Christfort, Tijs Slaats
Lecture notes in computer science (2023), pp. 3-19
Closed Access | Times Cited: 2
Log-Driven Conformance Checking Approximation Method Based on Machine Learning Model
Huan Fang, Sichen Zhang, Zhenhui Mei
International Journal of Advanced Computer Science and Applications (2024) Vol. 15, Iss. 7
Open Access
CoSMo: A Framework to Instantiate Conditioned Process Simulation Models
Rafael S. Oyamada, Gabriel Marques Tavares, Sylvio Barbon, et al.
Lecture notes in computer science (2024), pp. 328-344
Closed Access
A novel trace-based sampling method for conformance checking
Heidy M. Marin-Castro, Miguel Morales‐Sandoval, José Luis González-Compeán, et al.
PeerJ Computer Science (2024) Vol. 10, pp. e2601-e2601
Open Access
Automatic Population-Based Responsibility Modeling Using Process Mining: Application to Chronic Obstructive Pulmonary Disease
Ana Lucia Tula, Vincent Augusto, Xavier Boucher, et al.
2018 Winter Simulation Conference (WSC) (2024), pp. 964-975
Closed Access
Analysis, Evaluation and Reusability of Virtual Laboratory Software Based on Conceptual Modeling and Conformance Checking
Athanasios Sypsas, Dimitris Kalles
Mathematics (2023) Vol. 11, Iss. 9, pp. 2153-2153
Open Access | Times Cited: 1
Peer Review #3 of "Discovering generative models from event logs: data-driven simulation vs deep learning (v0.2)"
Manuel Camargo, Marlon Dumas, Oscar González-Rojas, et al.
(2021)
Open Access | Times Cited: 1
Peer Review #1 of "Discovering generative models from event logs: data-driven simulation vs deep learning (v0.2)"
Manuel Camargo, Marlon Dumas, Oscar González-Rojas, et al.
(2021)
Open Access
Peer Review #2 of "Discovering generative models from event logs: data-driven simulation vs deep learning (v0.2)"
Manuel Camargo, Marlon Dumas, Oscar González-Rojas, et al.
(2021)
Open Access
Peer Review #3 of "Discovering generative models from event logs: data-driven simulation vs deep learning (v0.1)"
Manuel Camargo, Marlon Dumas, Oscar González-Rojas, et al.
(2021)
Open Access
Peer Review #2 of "Discovering generative models from event logs: data-driven simulation vs deep learning (v0.1)"
Manuel Camargo, Marlon Dumas, Oscar González-Rojas, et al.
(2021)
Open Access
Peer Review #1 of "Discovering generative models from event logs: data-driven simulation vs deep learning (v0.1)"
Manuel Camargo, Marlon Dumas, Oscar González-Rojas, et al.
(2021)
Open Access
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