Publicação
Enhancing Manufacturing Process Discovery Through Sub-Process Optimization
| Resumo: | Manufacturing process discovery extracts insights from event logs recorded by Manufacturing Information Systems (MISs) to optimize operational processes. However existing process discovery techniques struggle with complex concurrency relations, resulting in imprecise sub-processes that compromise model accuracy. This paper proposes a novel enhancement to Inductive Miner (IM)-generated models by optimizing local imprecise structures in manufacturing process models. The method first identifies imprecise sub-processes and extracts their corresponding sub-logs. Then imprecise sub-processes are incrementally optimized using a frequency-based filter mechanism, generating multiple candidate models. Finally, the best-quality candidate model based on evaluation metrics is selected as the final output. The proposed technique has been implemented as an open source process mining toolkit ProM plugin and evaluated on six real-life manufacturing event logs. Experimental results demonstrate that it outperforms state-of-the-art techniques, producing higher quality process models, making it particularly suited for manufacturing process discovery. |
|---|---|
| Autores principais: | Yan, Jiaxin |
| Outros Autores: | Liu, Cong; Cheng, Long; Cheng, Jiujun; Ni, Weijian; Zeng, Qingtian |
| Assunto: | Imprecise Sub-process Opti-mization Inductive Miner Manufacturing Information Systems Petri Nets Process Discovery Information Systems Computer Science Applications Computer Networks and Communications Information Systems and Management Artificial Intelligence |
| Ano: | 2025 |
| País: | Portugal |
| Tipo de documento: | documento de conferência |
| Tipo de acesso: | acesso embargado |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| _version_ | 1879446606400454656 |
|---|---|
| author | Yan, Jiaxin |
| author2 | Liu, Cong Cheng, Long Cheng, Jiujun Ni, Weijian Zeng, Qingtian |
| author2_role | author author author author author |
| author_facet | Yan, Jiaxin Liu, Cong Cheng, Long Cheng, Jiujun Ni, Weijian Zeng, Qingtian |
| author_role | author |
| contributor_name_str_mv | NOVA Information Management School (NOVA IMS) Information Management Research Center (MagIC) - NOVA Information Management School RUN |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Yan, Jiaxin\"},{\"Person.name\":\"Liu, Cong\"},{\"Person.name\":\"Cheng, Long\"},{\"Person.name\":\"Cheng, Jiujun\"},{\"Person.name\":\"Ni, Weijian\"},{\"Person.name\":\"Zeng, Qingtian\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | NOVA Information Management School (NOVA IMS) Information Management Research Center (MagIC) - NOVA Information Management School RUN |
| datacite.creators.creator.creatorName.fl_str_mv | Yan, Jiaxin Liu, Cong Cheng, Long Cheng, Jiujun Ni, Weijian Zeng, Qingtian |
| datacite.date.Accepted.fl_str_mv | 2025-07-07T00:00:00Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_f1cf |
| datacite.subjects.subject.fl_str_mv | Imprecise Sub-process Opti-mization Inductive Miner Manufacturing Information Systems Petri Nets Process Discovery Information Systems Computer Science Applications Computer Networks and Communications Information Systems and Management Artificial Intelligence |
| datacite.titles.title.fl_str_mv | Enhancing Manufacturing Process Discovery Through Sub-Process Optimization |
| dc.contributor.none.fl_str_mv | NOVA Information Management School (NOVA IMS) Information Management Research Center (MagIC) - NOVA Information Management School RUN |
| dc.creator.none.fl_str_mv | Yan, Jiaxin Liu, Cong Cheng, Long Cheng, Jiujun Ni, Weijian Zeng, Qingtian |
| dc.date.Accepted.fl_str_mv | 2025-07-07T00:00:00Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10362/191474 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | Institute of Electrical and Electronics Engineers (IEEE) |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_f1cf |
| dc.subject.none.fl_str_mv | Imprecise Sub-process Opti-mization Inductive Miner Manufacturing Information Systems Petri Nets Process Discovery Information Systems Computer Science Applications Computer Networks and Communications Information Systems and Management Artificial Intelligence |
| dc.title.fl_str_mv | Enhancing Manufacturing Process Discovery Through Sub-Process Optimization |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_c94f |
| description | Manufacturing process discovery extracts insights from event logs recorded by Manufacturing Information Systems (MISs) to optimize operational processes. However existing process discovery techniques struggle with complex concurrency relations, resulting in imprecise sub-processes that compromise model accuracy. This paper proposes a novel enhancement to Inductive Miner (IM)-generated models by optimizing local imprecise structures in manufacturing process models. The method first identifies imprecise sub-processes and extracts their corresponding sub-logs. Then imprecise sub-processes are incrementally optimized using a frequency-based filter mechanism, generating multiple candidate models. Finally, the best-quality candidate model based on evaluation metrics is selected as the final output. The proposed technique has been implemented as an open source process mining toolkit ProM plugin and evaluated on six real-life manufacturing event logs. Experimental results demonstrate that it outperforms state-of-the-art techniques, producing higher quality process models, making it particularly suited for manufacturing process discovery. |
| dirty | 0 |
| eu_rights_str_mv | embargoedAccess |
| format | conferenceObject |
| fulltext.url.fl_str_mv | https://run.unl.pt/bitstreams/c65d7c95-a5b3-4bc6-8efb-ea32af94ef8c/download |
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| identifier.url.fl_str_mv | http://hdl.handle.net/10362/191474 |
| inst_facet_str | urn:organizationAcronym:unl{{{_:::_}}}Universidade Nova de Lisboa |
| instacron_str | unl |
| institution | Universidade Nova de Lisboa |
| instname_str | Universidade Nova de Lisboa |
| language | eng |
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| network_name_str | Repositório Institucional da UNL |
| oai_identifier_str | oai:run.unl.pt:10362/191474 |
| organization_str_mv | urn:organizationAcronym:unl |
| person_str_mv | Yan, Jiaxin Liu, Cong Cheng, Long Cheng, Jiujun Ni, Weijian Zeng, Qingtian |
| publishDate | 2025 |
| publisher.none.fl_str_mv | Institute of Electrical and Electronics Engineers (IEEE) |
| repo_facet_str | urn:repositoryAcronym:run{{{_:::_}}}Repositório Institucional da UNL |
| reponame_str | Repositório Institucional da UNL |
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| spelling | engInstitute of Electrical and Electronics Engineers (IEEE)enManufacturing process discovery extracts insights from event logs recorded by Manufacturing Information Systems (MISs) to optimize operational processes. However existing process discovery techniques struggle with complex concurrency relations, resulting in imprecise sub-processes that compromise model accuracy. This paper proposes a novel enhancement to Inductive Miner (IM)-generated models by optimizing local imprecise structures in manufacturing process models. The method first identifies imprecise sub-processes and extracts their corresponding sub-logs. Then imprecise sub-processes are incrementally optimized using a frequency-based filter mechanism, generating multiple candidate models. Finally, the best-quality candidate model based on evaluation metrics is selected as the final output. The proposed technique has been implemented as an open source process mining toolkit ProM plugin and evaluated on six real-life manufacturing event logs. Experimental results demonstrate that it outperforms state-of-the-art techniques, producing higher quality process models, making it particularly suited for manufacturing process discovery.application/pdfenEnhancing Manufacturing Process Discovery Through Sub-Process OptimizationYan, JiaxinLiu, CongCheng, LongCheng, JiujunNi, WeijianZeng, QingtianNOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolHostingInstitutionOrganizationalRUNe-mailmailto:run@unl.ptrun@unl.ptISBNIsPartOf979-8-3315-5563-4URNIsPartOfPURE: 133047566URNIsPartOfPURE UUID: 23d3e828-a2de-4400-b6f2-2446cf969b73URNIsPartOfScopus: 105018799155URNIsPartOfWOS: 001699536200051DOIIsPartOf10.1109/ICWS67624.2025.000612025-07-072025-07-07T00:00:00ZHandlehttp://hdl.handle.net/10362/191474http://purl.org/coar/access_right/c_f1cfembargoed accessImprecise Sub-process Opti-mizationInductive MinerManufacturing Information SystemsPetri NetsProcess DiscoveryInformation SystemsComputer Science ApplicationsComputer Networks and CommunicationsInformation Systems and ManagementArtificial Intelligence1470094 bytesother research producthttp://purl.org/coar/resource_type/c_c94fconference objecthttp://purl.org/coar/access_right/c_f1cfapplication/pdffulltexthttps://run.unl.pt/bitstreams/c65d7c95-a5b3-4bc6-8efb-ea32af94ef8c/download |
| spellingShingle | Enhancing Manufacturing Process Discovery Through Sub-Process Optimization Yan, Jiaxin Imprecise Sub-process Opti-mization Inductive Miner Manufacturing Information Systems Petri Nets Process Discovery Information Systems Computer Science Applications Computer Networks and Communications Information Systems and Management Artificial Intelligence |
| status | SINGLETON |
| subject.fl_str_mv | Imprecise Sub-process Opti-mization Inductive Miner Manufacturing Information Systems Petri Nets Process Discovery Information Systems Computer Science Applications Computer Networks and Communications Information Systems and Management Artificial Intelligence |
| title | Enhancing Manufacturing Process Discovery Through Sub-Process Optimization |
| title_full | Enhancing Manufacturing Process Discovery Through Sub-Process Optimization |
| title_fullStr | Enhancing Manufacturing Process Discovery Through Sub-Process Optimization |
| title_full_unstemmed | Enhancing Manufacturing Process Discovery Through Sub-Process Optimization |
| title_short | Enhancing Manufacturing Process Discovery Through Sub-Process Optimization |
| title_sort | Enhancing Manufacturing Process Discovery Through Sub-Process Optimization |
| topic | Imprecise Sub-process Opti-mization Inductive Miner Manufacturing Information Systems Petri Nets Process Discovery Information Systems Computer Science Applications Computer Networks and Communications Information Systems and Management Artificial Intelligence |
| topic_facet | Imprecise Sub-process Opti-mization Inductive Miner Manufacturing Information Systems Petri Nets Process Discovery Information Systems Computer Science Applications Computer Networks and Communications Information Systems and Management Artificial Intelligence |
| url | http://hdl.handle.net/10362/191474 |
| visible | 1 |
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