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EdgeIM

2025-07-07Documento de conferência Acesso embargado
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dc.contributor.authorSu, Xuan
dc.contributor.authorLiu, Cong
dc.contributor.authorLu, Faming
dc.contributor.authorCheng, Long
dc.contributor.authorZeng, Qingtian
dc.contributor.authorZhang, Shouli
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.coverage.spatialHelsinki, Finland
dc.date.accessioned2025-12-04T21:06:18Z
dc.date.embargoedUntil2027-09-30
dc.date.issued2025-07-07
dc.descriptionSu, X., Liu, C., Lu, F., Cheng, L., Zeng, Q., & Zhang, S. (2025). EdgeIM: An Efficient Edge-Based Process Model Discovery Technique. In R. N. Chang, C. K. Chang, J. Yang, N. Atukorala, D. Chen, S. Helal, S. Tarkoma, Q. He, T. Kosar, C. A. Ardagna, A. Beheshti, B. Cheng, & W. Gaaloul (Eds.), 2025 IEEE International Conference on Web Services: IEEE ICWS 2025 (pp. 404-410). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICWS67624.2025.00057
dc.description.abstractThe rapid expansion of Internet of Things (IoT) devices has led to an explosion of event data, posing significant challenges for traditional process model discovery techniques in terms of scalability and discovery accuracy. These techniques rely on centralized storage and processing, which are hindered by data transfer limitations, storage capacity, and computational overhead in distributed IoT environments. Edge-based model discovery techniques offer a promising solution for analyzing large-scale IoT data. However, existing techniques suffer from low efficiency and an inability to handle complex process structures. To address these challenges, we propose EdgeIM, an efficient edge-based process model discovery technique that enhances efficiency and model accuracy. EdgeIM operates in three key stages: preprocessing and feature-preserving sampling to eliminate redundant data, local processing at edge nodes to extract key structural features, and global feature aggregation at a central node for model discovery. EdgeIM has been implemented on the open-source process mining platform PM4Py, and experimental results on nine public event logs demonstrate that, compared to existing edge-based model discovery techniques, EdgeIM significantly improves discovery efficiency while maintaining high model quality.en
dc.description.versionauthorsversion
dc.description.versionpublished
dc.format.extent7
dc.format.extent567028
dc.identifier.doi10.1109/ICWS67624.2025.00057
dc.identifier.isbn979-8-3315-5563-4
dc.identifier.otherPURE: 133048059
dc.identifier.otherPURE UUID: 1fd3b5e1-9b98-4e49-b99f-fc609a146052
dc.identifier.otherScopus: 105018799720
dc.identifier.otherWOS: 001699536200047
dc.identifier.urihttp://hdl.handle.net/10362/191475
dc.identifier.urlhttps://www.scopus.com/pages/publications/105018799720
dc.identifier.urlhttps://www.webofscience.com/wos/woscc/full-record/WOS:001699536200047
dc.language.isoeng
dc.peerreviewedyes
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectEdge Computing
dc.subjectInternet of Things
dc.subjectModel Discovery
dc.subjectProcess Mining
dc.subjectInformation Systems
dc.subjectComputer Science Applications
dc.subjectComputer Networks and Communications
dc.subjectInformation Systems and Management
dc.subjectArtificial Intelligence
dc.titleEdgeIMen
dc.title.subtitleAn Efficient Edge-Based Process Model Discovery Techniqueen
dc.typeconference object
degois.publication.firstPage404
degois.publication.lastPage410
degois.publication.title2025 IEEE International Conference on Web Services
degois.publication.titleIEEE International Conference on Web Services (ICWS) 2025
dspace.entity.typePublication
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