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Challenges, Frameworks and Future Directions in Process Discovery: Navigating the Evolution of Process Discovery

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Resumo(s)

Process mining, a discipline that evolved from Business Process Management (BPM), has seen significant advancements, particularly in contrasting approaches such as process mining vs. task mining, case-centric vs. object-centric process mining, and the distinction between process models and process architectures. Core components of process mining include process discovery, conformance checking, and process enhancement. Among these, process discovery has become a focal point for automation efforts. Currently, automated process discovery incorporates domain knowledge, and future developments aim to deepen this integration, refining algorithms to enhance automation. To address the challenges in this domain, a Systematic Literature Review (SLR) was conducted within the framework of a thesis. Based on the findings, a recommendation framework for process discovery was proposed, emphasizing the critical roles of event log pre-processing and domain knowledge utilization in achieving reliable outcomes. The study aligns with existing literature to move closer to fully automated process discovery and provides a comprehensive mapping of algorithm families, detailing the specific challenges each addresses. This framework serves as a guideline for advancing automated and domain-informed process discovery methodologies.

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Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Information Analysis and Management

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Process model discovery Process mining Event logs Automation of process discovery Process discovery algorithms Domain-knowledge Quality databases Incomplete data Noise in event logs Process architecture Interrelated processes Design artifact High-quality process models SDG 8 - Decent work and economic growth SDG 9 - Industry, innovation and infrastructure

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