An Abstraction-Based Approach for Privacy-Aware Federated Process Mining
Process awareness is an essential success factor in any type of business. Process mining uses event data to discover and analyze actual business processes. Although process mining is growing fast and it has already become the basis for a plethora of commercial tools, research has not yet sufficientl...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10089448/ |
| _version_ | 1797844074006315008 |
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| author | Majid Rafiei Wil M. P. Van Der Aalst |
| author_facet | Majid Rafiei Wil M. P. Van Der Aalst |
| author_sort | Majid Rafiei |
| collection | DOAJ |
| description | Process awareness is an essential success factor in any type of business. Process mining uses event data to discover and analyze actual business processes. Although process mining is growing fast and it has already become the basis for a plethora of commercial tools, research has not yet sufficiently addressed the privacy concerns in this discipline. Most of the contributions made to privacy-preserving process mining consider an intra-organizational setting, where a single organization wants to safely publish its event data so that process mining experts can analyze the data and provide insights. However, in real-life settings, organizations need to collaborate for performing their processes, e.g., a supply chain process may involve many organizations. Therefore, event data and processes are often distributed over several partner organizations, yet organizations hesitate to share their data due to privacy and confidentiality concerns. In this paper, we introduce an abstraction-based approach to support privacy-aware process mining in inter-organizational settings. We implement our approach and demonstrate its effectiveness using real-life event logs. |
| first_indexed | 2024-04-09T17:16:32Z |
| format | Article |
| id | doaj.art-bf6f5a8ab80e417cb669fabadba801d0 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-09T17:16:32Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-bf6f5a8ab80e417cb669fabadba801d02023-04-19T23:00:09ZengIEEEIEEE Access2169-35362023-01-0111336973371410.1109/ACCESS.2023.326367310089448An Abstraction-Based Approach for Privacy-Aware Federated Process MiningMajid Rafiei0https://orcid.org/0000-0001-7161-6927Wil M. P. Van Der Aalst1https://orcid.org/0000-0002-0955-6940Chair of Process and Data Science, RWTH Aachen University, Aachen, GermanyChair of Process and Data Science, RWTH Aachen University, Aachen, GermanyProcess awareness is an essential success factor in any type of business. Process mining uses event data to discover and analyze actual business processes. Although process mining is growing fast and it has already become the basis for a plethora of commercial tools, research has not yet sufficiently addressed the privacy concerns in this discipline. Most of the contributions made to privacy-preserving process mining consider an intra-organizational setting, where a single organization wants to safely publish its event data so that process mining experts can analyze the data and provide insights. However, in real-life settings, organizations need to collaborate for performing their processes, e.g., a supply chain process may involve many organizations. Therefore, event data and processes are often distributed over several partner organizations, yet organizations hesitate to share their data due to privacy and confidentiality concerns. In this paper, we introduce an abstraction-based approach to support privacy-aware process mining in inter-organizational settings. We implement our approach and demonstrate its effectiveness using real-life event logs.https://ieeexplore.ieee.org/document/10089448/Confidentialityevent datafederated process mininginter-organizational process miningprivacy preservation |
| spellingShingle | Majid Rafiei Wil M. P. Van Der Aalst An Abstraction-Based Approach for Privacy-Aware Federated Process Mining IEEE Access Confidentiality event data federated process mining inter-organizational process mining privacy preservation |
| title | An Abstraction-Based Approach for Privacy-Aware Federated Process Mining |
| title_full | An Abstraction-Based Approach for Privacy-Aware Federated Process Mining |
| title_fullStr | An Abstraction-Based Approach for Privacy-Aware Federated Process Mining |
| title_full_unstemmed | An Abstraction-Based Approach for Privacy-Aware Federated Process Mining |
| title_short | An Abstraction-Based Approach for Privacy-Aware Federated Process Mining |
| title_sort | abstraction based approach for privacy aware federated process mining |
| topic | Confidentiality event data federated process mining inter-organizational process mining privacy preservation |
| url | https://ieeexplore.ieee.org/document/10089448/ |
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