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2027
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Title : Model-Semantics-Aware Predictive Process Monitoring Paradigm and its Empirical Validation |
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Time : 16:30 ~ |
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Speaker : Prof. Kwanghoon Pio Kim, Ph.D., Chair of TPC ICACT2027, Emeritus of AI & Computer Engineering at Kyonggi University
He is a Professor Emeritus in the School of AI Computer Science and Engineering at Kyonggi University, south Korea. He is the Founder and Director of both the Data and Process Engineering Research Laboratory and the Contents Convergence Software Research Institute. Under his leadership since 2007, the latter was designated as a National Science and Engineering Research Institute by the Ministry of Education (2020), securing long-term funding from the National Research Foundation through 2029. His scholarly work spans decades of innovation in process-centric computing, workflow management, and process mining. Recently, his research has expanded into cutting-edge AI, focusing on Predictive Process Monitoring, Vision-Language Models (VLMs), Large Language Models (LLMs), and AI-driven public safety solutions. He earned his Ph.D. from the University of Colorado Boulder and has contributed to the field internationally as a Country Chair for the Workflow Management Coalition and as President of the Korean Society for Internet and Information (2023–2024).
Abstract : Predictive Process Monitoring (PPM) has recently achieved remarkable advances through deep learning models that learn future process behavior directly from historical event logs. Nevertheless, existing predictive process monitoring approaches remain fundamentally log-centric: they implicitly assume that system-enacted process traces faithfully represent the behavioral semantics of the underlying business process. In practice, however, event logs capture only individual execution instances, whereas business process models explicitly define the logical, behavioral, temporal, organizational, and operational semantics governing all valid executions. Consequently, predictive models frequently learn execution-specific artifacts rather than the model-defined process semantics that should determine future process behavior.
This tutorial introduces a new predictive process monitoring paradigm, called Model-Semantics-Aware Predictive Process Monitoring (MSA-PPM), which systematically bridges process mining and predictive learning through explicit semantic knowledge integration. The proposed paradigm introduces a unified semantic knowledge representation, referred to as the Process Semantics Dictionary (PSD), that organizes model-defined process semantics, including parallel, exclusive-choice, loop, synchronization, temporal, resource, organizational, and constraint semantics. Guided by the semantic knowledge represented in the PSD, conventional system-enacted process traces are transformed into model-semantics-aware process traces through a semantic preprocessing layer prior to predictive learning. In particular, as the first operational realization of this new paradigm, this tutorial demonstrates empirical validation through the implementation of the parallel-semantics module within the PSD.
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