Video Process Mining and Model Matching for Intelligent Development: Conformance Checking
Traditional business process-extraction models mainly rely on structured data such as logs, which are difficult to apply to unstructured data such as images and videos, making it impossible to perform process extractions in many data scenarios. Moreover, the generated process model lacks analysis co...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/8/3812 |
| _version_ | 1827743771463254016 |
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| author | Shuang Chen Minghao Zou Rui Cao Ziqi Zhao Qingtian Zeng |
| author_facet | Shuang Chen Minghao Zou Rui Cao Ziqi Zhao Qingtian Zeng |
| author_sort | Shuang Chen |
| collection | DOAJ |
| description | Traditional business process-extraction models mainly rely on structured data such as logs, which are difficult to apply to unstructured data such as images and videos, making it impossible to perform process extractions in many data scenarios. Moreover, the generated process model lacks analysis consistency of the process model, resulting in a single understanding of the process model. To solve these two problems, a method of extracting process models from videos and analyzing the consistency of process models is proposed. Video data are widely used to capture the actual performance of business operations and are key sources of business data. Video data preprocessing, action placement and recognition, predetermined models, and conformance verification are all included in a method for extracting a process model from videos and analyzing the consistency between the process model and the predefined model. Finally, the similarity was calculated using graph edit distances and adjacency relationships (<i>GED_NAR</i>). The experimental results showed that the process model mined from the video was better in line with how the business was actually carried out than the process model derived from the noisy process logs. |
| first_indexed | 2024-03-11T04:34:14Z |
| format | Article |
| id | doaj.art-9f91dbdbbdf34d0791aac1254913f4bd |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T04:34:14Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-9f91dbdbbdf34d0791aac1254913f4bd2023-11-17T21:14:54ZengMDPI AGSensors1424-82202023-04-01238381210.3390/s23083812Video Process Mining and Model Matching for Intelligent Development: Conformance CheckingShuang Chen0Minghao Zou1Rui Cao2Ziqi Zhao3Qingtian Zeng4School of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266000, ChinaSchool of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266000, ChinaSchool of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266000, ChinaSchool of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266000, ChinaSchool of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266000, ChinaTraditional business process-extraction models mainly rely on structured data such as logs, which are difficult to apply to unstructured data such as images and videos, making it impossible to perform process extractions in many data scenarios. Moreover, the generated process model lacks analysis consistency of the process model, resulting in a single understanding of the process model. To solve these two problems, a method of extracting process models from videos and analyzing the consistency of process models is proposed. Video data are widely used to capture the actual performance of business operations and are key sources of business data. Video data preprocessing, action placement and recognition, predetermined models, and conformance verification are all included in a method for extracting a process model from videos and analyzing the consistency between the process model and the predefined model. Finally, the similarity was calculated using graph edit distances and adjacency relationships (<i>GED_NAR</i>). The experimental results showed that the process model mined from the video was better in line with how the business was actually carried out than the process model derived from the noisy process logs.https://www.mdpi.com/1424-8220/23/8/3812video processingprocess modelPetri netconformance checking |
| spellingShingle | Shuang Chen Minghao Zou Rui Cao Ziqi Zhao Qingtian Zeng Video Process Mining and Model Matching for Intelligent Development: Conformance Checking Sensors video processing process model Petri net conformance checking |
| title | Video Process Mining and Model Matching for Intelligent Development: Conformance Checking |
| title_full | Video Process Mining and Model Matching for Intelligent Development: Conformance Checking |
| title_fullStr | Video Process Mining and Model Matching for Intelligent Development: Conformance Checking |
| title_full_unstemmed | Video Process Mining and Model Matching for Intelligent Development: Conformance Checking |
| title_short | Video Process Mining and Model Matching for Intelligent Development: Conformance Checking |
| title_sort | video process mining and model matching for intelligent development conformance checking |
| topic | video processing process model Petri net conformance checking |
| url | https://www.mdpi.com/1424-8220/23/8/3812 |
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