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ORCIDMahsa Pourbafrani
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2020 – today
- 2024
[j2]Harry H. Beyel
, Omar Makke, Mahsa Pourbafrani, Oleg Gusikhin, Wil M. P. van der Aalst:
Analyzing Data Streams from Cyber-Physical-Systems: A Case Study. SN Comput. Sci. 5(6): 706 (2024)- 2023
- [c20]Mahsa Pourbafrani, Niels Lücking, Matthieu Lucke, Wil M. P. van der Aalst:
Steady State Estimation for Business Process Simulations. BPM (Forum) 2023: 178-195 - [c19]Mahsa Pourbafrani, Wil M. P. van der Aalst:
Data-Driven Simulation In Process Mining: Introducing A Reference Model. ECMS 2023: 411-420 - [c18]Benedikt Knopp, Mahsa Pourbafrani, Wil M. P. van der Aalst:
Discovering Object-Centric Process Simulation Models. ICPM 2023: 81-88 - [c17]Majid Rafiei, Duygu Bayrak, Mahsa Pourbafrani, Gyunam Park, Hayyan Helal, Gerhard Lakemeyer, Wil M. P. van der Aalst:
Extracting Rules from Event Data for Study Planning. ICPM Workshops 2023: 361-374 - [c16]Majid Rafiei, Frederik Wangelik, Mahsa Pourbafrani, Wil M. P. van der Aalst:
TraVaG: Differentially Private Trace Variant Generation Using GANs. RCIS 2023: 415-431 - [i7]Majid Rafiei, Frederik Wangelik, Mahsa Pourbafrani, Wil M. P. van der Aalst:
TraVaG: Differentially Private Trace Variant Generation Using GANs. CoRR abs/2303.16704 (2023) - [i6]Majid Rafiei, Duygu Bayrak, Mahsa Pourbafrani, Gyunam Park, Hayyan Helal, Gerhard Lakemeyer, Wil M. P. van der Aalst:
Extracting Rules from Event Data for Study Planning. CoRR abs/2310.02735 (2023) - 2022
- [j1]Mahsa Pourbafrani, Wil M. P. van der Aalst:
Discovering System Dynamics Simulation Models Using Process Mining. IEEE Access 10: 78527-78547 (2022) - [c15]Mahsa Pourbafrani, Firas Gharbi, Wil M. P. van der Aalst:
Process Diagnostics at Coarse-grained Levels. ICEIS (1) 2022: 484-491 - [c14]Mahsa Pourbafrani, Firas Gharbi, Wil M. P. van der Aalst:
A Tool for Business Processes Diagnostics. ICSOC Workshops 2022: 350-354 - [c13]Mahsa Pourbafrani, Wil M. P. van der Aalst:
Hybrid Business Process Simulation: Updating Detailed Process Simulation Models Using High-Level Simulations. RCIS 2022: 177-194 - [c12]Mahsa Pourbafrani, Majid Rafiei, Alessandro Berti, Wil M. P. van der Aalst:
Interactive Business Process Comparison Using Conformance and Performance Insights - A Tool. RCIS 2022: 735-743 - [i5]Mahsa Pourbafrani, Wil M. P. van der Aalst:
Interactive Process Improvement using Simulation of Enriched Process Trees. CoRR abs/2201.07755 (2022) - 2021
- [c11]Mahsa Pourbafrani, Wil M. P. van der Aalst:
Forward-Looking Process Mining. BPM (PhD/Demos) 2021: 56-61 - [c10]Mahsa Pourbafrani, Wil M. P. van der Aalst:
Extracting Process Features from Event Logs to Learn Coarse-Grained Simulation Models. CAiSE 2021: 125-140 - [c9]Mahsa Pourbafrani, Shreya Kar, Sebastian Kaiser, Wil M. P. van der Aalst:
Remaining Time Prediction for Processes with Inter-case Dynamics. ICPM Workshops 2021: 140-153 - [c8]Mahsa Pourbafrani, Wil M. P. van der Aalst:
Interactive Process Improvement Using Simulation of Enriched Process Trees. ICSOC Workshops 2021: 61-76 - [c7]Mahsa Pourbafrani, Shuai Jiao, Wil M. P. van der Aalst:
SIMPT: Process Improvement Using Interactive Simulation of Time-Aware Process Trees. RCIS 2021: 588-594 - [i4]Mahsa Pourbafrani, Sandhya Vasudevan, Faizan Zafar, Yuan Xingran, Ravikumar Singh, Wil M. P. van der Aalst:
A Python Extension to Simulate Petri nets in Process Mining. CoRR abs/2102.08774 (2021) - [i3]Wil M. P. van der Aalst, Tobias Brockhoff, Anahita Farhang Ghahfarokhi, Mahsa Pourbafrani, Merih Seran Uysal, Sebastiaan J. van Zelst:
Removing Operational Friction Using Process Mining: Challenges Provided by the Internet of Production (IoP). CoRR abs/2107.13066 (2021) - [i2]Mahsa Pourbafrani, Shuai Jiao, Wil M. P. van der Aalst:
SIMPT: Process Improvement Using Interactive Simulation of Time-aware Process Trees. CoRR abs/2108.02052 (2021) - 2020
- [c6]Mahsa Pourbafrani, Sebastiaan J. van Zelst, Wil M. P. van der Aalst:
Supporting Automatic System Dynamics Model Generation for Simulation in the Context of Process Mining. BIS 2020: 249-263 - [c5]Mahsa Pourbafrani, Wil M. P. van der Aalst:
PMSD: Data-Driven Simulation Using System Dynamics and Process Mining. BPM (PhD/Demos) 2020: 77-81 - [c4]Wil M. P. van der Aalst, Tobias Brockhoff, Anahita Farhang Ghahfarokhi, Mahsa Pourbafrani, Merih Seran Uysal, Sebastiaan J. van Zelst:
Removing Operational Friction Using Process Mining: Challenges Provided by the Internet of Production (IoP). DATA (Revised Selected Papers) 2020: 1-31 - [c3]Mahsa Pourbafrani, Sebastiaan J. van Zelst, Wil M. P. van der Aalst:
Semi-automated Time-Granularity Detection for Data-Driven Simulation Using Process Mining and System Dynamics. ER 2020: 77-91 - [c2]Mahsa Pourbafrani, Sebastiaan J. van Zelst, Wil M. P. van der Aalst:
Supporting Decisions in Production Line Processes by Combining Process Mining and System Dynamics. IHSI 2020: 461-467 - [i1]Mahsa Pourbafrani, Wil M. P. van der Aalst:
PMSD: Data-Driven Simulation Using System Dynamics and Process Mining. CoRR abs/2010.00943 (2020)
2010 – 2019
- 2019
- [c1]Mahsa Pourbafrani, Sebastiaan J. van Zelst, Wil M. P. van der Aalst:
Scenario-Based Prediction of Business Processes Using System Dynamics. OTM Conferences 2019: 422-439
Coauthor Index
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