"决策树学习在业务流程事件日志缺失趋势提取中的应用研究"

"Extraction of Missing Tendency Using Decision Tree Learning in Business Process Event Log" is a research paper that focuses on utilizing decision tree learning to extract missing tendencies from business process event logs. With the increasing popularity of process mining in recent years, the importance of analyzing event logs to enhance business operations has become apparent. However, event logs often suffer from missing data caused by technical or human errors, leading to incomplete analysis results. Traditionally, prediction models are used to impute missing data in event logs. However, these methods may not be accurate or efficient in capturing the underlying trends and patterns present in the data. This paper proposes the use of decision tree learning as a more effective approach to identify and extract missing tendencies from event logs. By leveraging the hierarchical structure of decision trees, the algorithm can uncover hidden patterns and relationships within the data, leading to more accurate imputations and a deeper understanding of the underlying processes. The abstract of the paper highlights the significance of this research in the field of process mining and emphasizes the potential impact of using decision tree learning to address the issue of missing data in event logs. By improving the accuracy and reliability of analysis results, businesses can make more informed decisions and optimize their process operations for better efficiency and performance. Overall, "Extraction of Missing Tendency Using Decision Tree Learning in Business Process Event Log" offers a novel and practical solution to a common challenge faced in process mining, demonstrating the potential of decision tree learning to enhance data analysis and drive business success."