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Inductive Context-aware Process Discovery

By Dafna Schumacher
Location Bloomfield 424, Faculty of Industrial Engineering and Management
Advisor(s): Avi Gal
Academic Program: Please choose
Tuesday 18 June 2019, 12:30 - 13:30

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 evaluate the approach using synthetic and real world datasets and show that the resulting models are superior to state-of-the-art discovery methods in terms of measures that are based on multi-perspective alignments.