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Providing appropriate structures around human resources can streamlineoperations and thus facilitate the competitiveness of an organization. Toachieve this goal, modern organizations need to acquire an accurate and timelyunderstanding of human resource grouping while faced with an ever-changingenvironment. The use of process mining offers a promising way to help addressthe need through utilizing event log data stored in information systems. Byextracting knowledge about the actual behavior of resources participating inbusiness processes from event logs, organizational models can be constructed,which facilitate the analysis of the de facto grouping of human resourcesrelevant to process execution. Nevertheless, open research gaps remain to beaddressed when applying the state-of-the-art process mining to analyze resourcegrouping. For one, the discovery of organizational models has only limitedconnections with the context of process execution. For another, a rigoroussolution that evaluates organizational models against event log data is yet tobe proposed. In this paper, we aim to tackle these research challenges bydeveloping a novel framework built upon a richer definition of organizationalmodels coupling resource grouping with process execution knowledge. Byintroducing notions of conformance checking for organizational models, theframework allows effective evaluation of organizational models, and thereforeprovides a foundation for analyzing and improving resource grouping based onevent logs. We demonstrate the feasibility of this framework by proposing anapproach underpinned by the framework for organizational model discovery, andalso conduct experiments on real-life event logs to discover and evaluateorganizational models.

OrgMining 2.0: A Novel Framework for Organizational Model Mining from Event Logs