Process discovery with context-aware process trees. Issue 106 (May 2022) - Shraga, Roee; Gal, Avigdor; Schumacher, Dafna; Senderovich, Arik; Weidlich, Matthias
Abstract: 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. Highlights: Context-aware process trees (CaT) are defined. An inductive context-aware discovery algorithm (CaDi) is proposed. CaDi produces trees that are context consistent, deterministic, and complete. CaDi discovers models of higher quality than the state-of-the-art. CaDi can be tuned to provide CaTs that are more explainable.