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پژوهشنامه پردازش و مدیریت اطلاعات، جلد ۴۰، شماره ویژه نامه انگلیسی۳، صفحات ۰-۰
عنوان فارسی Quality Metrics for Business Process Event Logs Based on High Frequency Traces
چکیده فارسی مقاله In today's data-centric business landscape, characterized by the omnipresence of Advanced Business Intelligence and Data Science technologies, the practice of Process Mining takes center stage in Business Process Management. This study addresses the critical challenge of ensuring the quality of event logs, which serve as the foundational data source for Process Mining. Event logs, derived from interactions among process participants and information systems, offer profound insights into the authentic behavior of business processes, reflecting the organizational rules, procedures, norms, and culture. However, the quality of these event logs is often hindered by interactions between different actors and systems. In response, our research introduces a systematic approach, leveraging Python and the pm4py library for data analysis, employing trace filtering techniques, and utilizing Petri nets for process model representation. This psper proposes a methodology demonstrating a notable enhancement in the quality metrics of extracted subprocesses through trace filtering. Comparative analyses between original logs and filtered logs reveal improvements in fitness, precision, generalization, and simplicity, underscoring the practical significance of trace filtering in refining complex process models. These findings provide actionable insights for practitioners and researchers engaged in process mining and modeling, emphasizing the importance of data quality in deriving accurate and reliable business process insights.
کلیدواژه‌های فارسی مقاله کیفیت اطلاعات،عملکرد نوآورانه،تسهیم دانش،مدیریت فرایند کسب و کار،شرکت های کوچک و متوسط،
عنوان انگلیسی Quality Metrics for Business Process Event Logs Based on High Frequency Traces
چکیده انگلیسی مقاله In today's data-centric business landscape, characterized by the omnipresence of Advanced Business Intelligence and Data Science technologies, the practice of Process Mining takes center stage in Business Process Management. This study addresses the critical challenge of ensuring the quality of event logs, which serve as the foundational data source for Process Mining. Event logs, derived from interactions among process participants and information systems, offer profound insights into the authentic behavior of business processes, reflecting the organizational rules, procedures, norms, and culture. However, the quality of these event logs is often hindered by interactions between different actors and systems. In response, our research introduces a systematic approach, leveraging Python and the pm4py library for data analysis, employing trace filtering techniques, and utilizing Petri nets for process model representation. This psper proposes a methodology demonstrating a notable enhancement in the quality metrics of extracted subprocesses through trace filtering. Comparative analyses between original logs and filtered logs reveal improvements in fitness, precision, generalization, and simplicity, underscoring the practical significance of trace filtering in refining complex process models. These findings provide actionable insights for practitioners and researchers engaged in process mining and modeling, emphasizing the importance of data quality in deriving accurate and reliable business process insights.
کلیدواژه‌های انگلیسی مقاله process quality,quality metrics,business process model,event log
نویسندگان مقاله محسن محمدی |
گروه کامپیوتر مجتمع آموزش عالی فنی-مهندسی اسفراین

نشانی اینترنتی https://jipm.irandoc.ac.ir/article_722067_27fdd547ed133d1b1dc8d623eeb8cea6.pdf
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