Process discovery with context-aware process trees

doi:10.1016/j.is.2020.101533

Process discovery with context-aware process trees

2022-05-01

8

PRE

AI

DOI：10.1016/j.is.2020.101533

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Discovery plays a key role in data-driven analysis of business processes. The vast majority of contemporary discovery algorithms aims at the identification of control-flow constructs. The increase in data richness, however, enables discovery that incorporates the context of process execution beyond the control-flow perspective. A control-flow first approach, where context data serves for refinement and annotation, is limited and fails to detect fundamental changes in the control-flow that depend on context data. In this work, we thus propose a novel approach for combining the control-flow and data perspectives under a single roof by extending inductive process discovery. Our approach provides criteria under which context data, handled through unsupervised learning, take priority over control flow in guiding process discovery. The resulting model is a process tree, in which some operators carry data semantics instead of control-flow semantics. We show that the proposed approach produces trees that are context consistent, deterministic, complete, and can be explainable without a major quality reduction. We evaluate the approach using synthetic and real-world datasets, showing that the resulting models are superior to state-of-the-art discovery methods in terms of measures based on multi-perspective alignments. (C)& nbsp;2020 Elsevier Ltd. All rights reserved.

Keyword:

Process discovery

Inductive mining

Context-aware process trees

Process discovery with context-aware process trees

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