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Discovering Work Specialization through Process Mining
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AI
DOI：10.1109/tsc.2026.3717882.png)
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Abstract
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The success of an organization depends on how well its workforce is mobilized and managed. Many organizations are taking up workforce analytics to support critical decision-making on their workforce. Process mining can empower workforce analytics, using event logs to extract accurate insights into employee performance and collaboration. To achieve precise analysis outcomes, it is imperative to consider work specialization, i.e., division of labor among human resources. In process mining, execution contexts can be used to characterize how specialization manifests across multiple process dimensions and therefore enhance resource-oriented analyses, such as organizational model mining and analysis of resource group work profiles. However, discovering execution contexts from event logs with minimal domain expertise remains a challenge. In this paper, we address this challenge by proposing a systematic approach to learn execution contexts. We evaluate the effectiveness of the approach through extensive experiments on real-life event logs. An additional case study shows the usefulness of the approach to support workforce analytics in the context of process execution.
Keywords:
process mining
event log
resource behavior
work specialization
execution context
workforce analytics
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