A process-aware framework to support Process Mining from blockchain applications
Several studies were conducted to demonstrate the application of Process Mining (PM) techniques to Ethereum-compatible application event data. However, the availability of event data is constrained by the application’s process awareness, which is under-reported in the literature. Based on domain ana...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-02-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157824000454 |
| _version_ | 1797272486478348288 |
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| author | Fouzia Alzhrani Kawther Saeedi Liping Zhao |
| author_facet | Fouzia Alzhrani Kawther Saeedi Liping Zhao |
| author_sort | Fouzia Alzhrani |
| collection | DOAJ |
| description | Several studies were conducted to demonstrate the application of Process Mining (PM) techniques to Ethereum-compatible application event data. However, the availability of event data is constrained by the application’s process awareness, which is under-reported in the literature. Based on domain analysis, which identified several challenges to mining the business process from blockchain applications, a framework was designed, instantiated, and tested in this study. The framework supports identification of appropriate cases for PM and automates the generation of event logs from blockchain data. It consists of two modules, the Process Awareness Recognizer (PAR) and the Event Log Generator (ELG). PAR is a rule-based classifier to assess the process awareness of a given application. ELG is an automated batch processing model consisting of three methods: (1) Extractor: to retrieve event data from blockchains; (2) Decoder: to transform the extracted data to a human-readable format; and (3) Formatter: to produce event log files in a format compatible with PM tools. It was validated by implementing a proof-of-concept application with an input set of 201 real-world applications. The results prove the framework’s feasibility and applicability. |
| first_indexed | 2024-03-07T14:30:02Z |
| format | Article |
| id | doaj.art-6275e931746242cca759d9b44eb28019 |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-03-07T14:30:02Z |
| publishDate | 2024-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-6275e931746242cca759d9b44eb280192024-03-06T05:25:43ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782024-02-01362101956A process-aware framework to support Process Mining from blockchain applicationsFouzia Alzhrani0Kawther Saeedi1Liping Zhao2Information Systems Department, King Abdulaziz University, Jeddah 21589, Saudi Arabia; Department of Computer Science, The University of Manchester, M13 9PL, United Kingdom; Corresponding author at: Information Systems Department, King Abdulaziz University, Jeddah 21589, Saudi Arabia.Information Systems Department, King Abdulaziz University, Jeddah 21589, Saudi ArabiaDepartment of Computer Science, The University of Manchester, M13 9PL, United KingdomSeveral studies were conducted to demonstrate the application of Process Mining (PM) techniques to Ethereum-compatible application event data. However, the availability of event data is constrained by the application’s process awareness, which is under-reported in the literature. Based on domain analysis, which identified several challenges to mining the business process from blockchain applications, a framework was designed, instantiated, and tested in this study. The framework supports identification of appropriate cases for PM and automates the generation of event logs from blockchain data. It consists of two modules, the Process Awareness Recognizer (PAR) and the Event Log Generator (ELG). PAR is a rule-based classifier to assess the process awareness of a given application. ELG is an automated batch processing model consisting of three methods: (1) Extractor: to retrieve event data from blockchains; (2) Decoder: to transform the extracted data to a human-readable format; and (3) Formatter: to produce event log files in a format compatible with PM tools. It was validated by implementing a proof-of-concept application with an input set of 201 real-world applications. The results prove the framework’s feasibility and applicability.http://www.sciencedirect.com/science/article/pii/S1319157824000454BlockchainEthereum Virtual Machine (EVM)Process automationEvent dataDecision supportRule-based system |
| spellingShingle | Fouzia Alzhrani Kawther Saeedi Liping Zhao A process-aware framework to support Process Mining from blockchain applications Journal of King Saud University: Computer and Information Sciences Blockchain Ethereum Virtual Machine (EVM) Process automation Event data Decision support Rule-based system |
| title | A process-aware framework to support Process Mining from blockchain applications |
| title_full | A process-aware framework to support Process Mining from blockchain applications |
| title_fullStr | A process-aware framework to support Process Mining from blockchain applications |
| title_full_unstemmed | A process-aware framework to support Process Mining from blockchain applications |
| title_short | A process-aware framework to support Process Mining from blockchain applications |
| title_sort | process aware framework to support process mining from blockchain applications |
| topic | Blockchain Ethereum Virtual Machine (EVM) Process automation Event data Decision support Rule-based system |
| url | http://www.sciencedirect.com/science/article/pii/S1319157824000454 |
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