Advancing Object-Centric Process Mining with Multi-Dimensional Data Operations

Advancing Object-Centric Process Mining with Multi-Dimensional Data Operations | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Advancing Object-Centric Process Mining with Multi-Dimensional Data Operations Shahrzad Khayatbashi, Najmeh Miri, Amin Jalali This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8444775/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Analyzing process data at varying levels of granularity is important to derive actionable insights and support informed decision-making. Object-Centric Event Data (OCED) enhances process mining by capturing interactions among multiple objects within events, leading to the discovery of more detailed and realistic yet complex process models. The lack of methods to adjust the granularity of the analysis limits users to leverage the full potential of Object-Centric Process Mining (OCPM). To address this gap, we propose four operations: drill-down, roll-up, unfold, and fold, which enable changing the granularity of analysis when working with Object-Centric Event Logs (OCEL). These operations allow analysts to seamlessly transition between detailed and aggregated process models, facilitating the discovery of insights that require varying levels of abstraction. We formally define these operations and implement them in an open-source Python library. To validate their utility, we applied the approach to real-world OCEL data extracted from a learning management system that covered a four-year period and approximately 400 students. Our evaluation demonstrates significant improvements in precision and fitness metrics for models discovered before and after applying these operations. This approach can empower analysts to perform more flexible and comprehensive process exploration, unlocking actionable insights through adaptable granularity adjustments. Object-Centric Process Mining Process Mining Methodology Log Extraction Learning Management System Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 01 Mar, 2026 Reviews received at journal 14 Feb, 2026 Reviews received at journal 27 Jan, 2026 Reviewers agreed at journal 27 Jan, 2026 Reviewers agreed at journal 27 Jan, 2026 Reviewers agreed at journal 22 Jan, 2026 Reviewers agreed at journal 22 Jan, 2026 Reviewers invited by journal 19 Jan, 2026 Editor assigned by journal 30 Dec, 2025 Submission checks completed at journal 25 Dec, 2025 First submitted to journal 24 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8444775","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":577419432,"identity":"b5466c84-3f0d-4fe3-9017-d2871bca5cfc","order_by":0,"name":"Shahrzad Khayatbashi","email":"","orcid":"","institution":"Linköping University","correspondingAuthor":false,"prefix":"","firstName":"Shahrzad","middleName":"","lastName":"Khayatbashi","suffix":""},{"id":577419436,"identity":"e963fde4-751a-4ab4-973c-e4970134e218","order_by":1,"name":"Najmeh Miri","email":"","orcid":"","institution":"Stockholm 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Operations","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"knowledge-and-information-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"kais","sideBox":"Learn more about [Knowledge and Information Systems](http://link.springer.com/journal/10115)","snPcode":"10115","submissionUrl":"https://submission.nature.com/new-submission/10115/3","title":"Knowledge and Information Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Object-Centric Process Mining, Process Mining Methodology, Log Extraction, Learning Management System","lastPublishedDoi":"10.21203/rs.3.rs-8444775/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8444775/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAnalyzing process data at varying levels of granularity is important to derive actionable insights and support informed decision-making. Object-Centric Event Data (OCED) enhances process mining by capturing interactions among multiple objects within events, leading to the discovery of more detailed and realistic yet complex process models. The lack of methods to adjust the granularity of the analysis limits users to leverage the full potential of Object-Centric Process Mining (OCPM). To address this gap, we propose four operations: drill-down, roll-up, unfold, and fold, which enable changing the granularity of analysis when working with Object-Centric Event Logs (OCEL). These operations allow analysts to seamlessly transition between detailed and aggregated process models, facilitating the discovery of insights that require varying levels of abstraction. We formally define these operations and implement them in an open-source Python library. To validate their utility, we applied the approach to real-world OCEL data extracted from a learning management system that covered a four-year period and approximately 400 students. Our evaluation demonstrates significant improvements in precision and fitness metrics for models discovered before and after applying these operations. This approach can empower analysts to perform more flexible and comprehensive process exploration, unlocking actionable insights through adaptable granularity adjustments.\u003c/p\u003e","manuscriptTitle":"Advancing Object-Centric Process Mining with Multi-Dimensional Data Operations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-21 07:31:46","doi":"10.21203/rs.3.rs-8444775/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-02T01:15:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-14T16:19:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-27T12:27:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"250689932429311085848546185556104560244","date":"2026-01-27T10:49:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"46587908199314598951006754813600433055","date":"2026-01-27T10:08:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"30723293152515867879996548437500250855","date":"2026-01-23T04:24:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"95533624716997753716609655730909091465","date":"2026-01-22T07:16:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-20T03:18:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-30T15:07:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-25T05:26:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Knowledge and Information Systems","date":"2025-12-24T18:41:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"knowledge-and-information-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"kais","sideBox":"Learn more about [Knowledge and Information Systems](http://link.springer.com/journal/10115)","snPcode":"10115","submissionUrl":"https://submission.nature.com/new-submission/10115/3","title":"Knowledge and Information Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d8a485ff-23bf-474b-a289-15d3af971734","owner":[],"postedDate":"January 21st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-31T02:08:27+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-21 07:31:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8444775","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8444775","identity":"rs-8444775","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}