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Toward Efficient Support for Business Process Event Log Sampling

2026-04Artigo científico Acesso aberto
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dc.contributor.authorSu, Xuan
dc.contributor.authorLiu, Cong
dc.contributor.authorZhang, Shuaipeng
dc.contributor.authorZeng, Qingtian
dc.contributor.authorMo, Qi
dc.contributor.authorCheng, Long
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.contributor.pblInstitute of Electrical and Electronics Engineers (IEEE)
dc.date.accessioned2026-04-20T23:09:47Z
dc.date.available2026-04-20T23:09:47Z
dc.date.issued2026-04
dc.descriptionSu, X., Liu, C., Zhang, S., Zeng, Q., Mo, Q., & Cheng, L. (2026). Toward Efficient Support for Business Process Event Log Sampling. IEEE Transactions on Services Computing, 19(2), 1606-1618. https://doi.org/10.1109/TSC.2026.3665370
dc.description.abstractLarge volumes of event logs have been accumulated by business information systems. Accompanied by that, various process discovery techniques are invented to uncover underlying business processes based on event logs. Event log sampling, recognized as one of the most effective techniques for accelerating discovery efficiency, has gained significant attention in recent days. However, achieving high performance in sampling while maintaining superior sample log quality remains a challenge for current techniques. To tackle the problem, a novel event log sampling technique, denoted as sigRank, is introduced to improve both the sampling efficiency and the quality of the sample log by quantifying the significance of each trace. The proposed sampling technique has been implemented as a publicly available tool in the open-source process mining platform ProM. Compared with state-of-the-art techniques using 12 public event logs, we experimentally illustrate that the proposed approach can significantly accelerate sampling efficiency while guaranteeing superior sample log quality for process discovery.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent13
dc.format.extent4405029
dc.identifier.doi10.1109/TSC.2026.3665370
dc.identifier.issn1939-1374
dc.identifier.otherPURE: 152718378
dc.identifier.otherPURE UUID: 5458d064-9d1a-4e5a-81df-5cc48f3613c2
dc.identifier.otherScopus: 105030678825
dc.identifier.otherWOS: 001737537400041
dc.identifier.urihttp://hdl.handle.net/10362/202395
dc.identifier.urlhttps://www.scopus.com/pages/publications/105030678825
dc.identifier.urlhttps://www.webofscience.com/wos/woscc/full-record/WOS:001737537400041
dc.language.isoeng
dc.peerreviewedyes
dc.relationhttps://doi.org/10.54499/UID/04152/2025
dc.relationhttps://doi.org/10.54499/UID/PRR/04152/2025
dc.subjectEfficiency
dc.subjectEvent Log Sampling
dc.subjectPetri Nets
dc.subjectProcess Discovery
dc.subjectQuality Evaluation
dc.subjectHardware and Architecture
dc.subjectComputer Science Applications
dc.subjectComputer Networks and Communications
dc.subjectInformation Systems and Management
dc.subjectSDG 9 - Industry, Innovation, and Infrastructure
dc.subjectSDG 12 - Responsible Consumption and Production
dc.titleToward Efficient Support for Business Process Event Log Samplingen
dc.typejournal article
degois.publication.firstPage1606
degois.publication.issue2
degois.publication.lastPage1618
degois.publication.titleIEEE Transactions on Services Computing
degois.publication.volume19
dspace.entity.typePublication
rcaap.rightsopenAccess

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NIMS: MagIC - Artigos em revista internacional com arbitragem científica (Peer-Review articles in international journals)