用pm4py分析交通罚单流程，挖掘执行规律优化管理
Navigating Process Mining: A Case study using pm4py
- 基于pm4py库分析事件日志，识别活动分布与流程变体
- 应用Alpha、Inductive、Heuristic等算法发现流程模型
- 适合流程优化与数据驱动决策的从业者参考
流程挖掘技术已成为分析事件数据以洞察业务流程的强大工具。本文使用Python中的pm4py库，对道路罚单管理流程进行综合分析。首先导入事件日志数据集，探索其特征，包括活动分布与流程变体。通过过滤与统计分析，揭示流程执行中的关键模式与差异。随后，应用Alpha Miner、Inductive Miner和Heuristic Miner等多种流程挖掘算法，从事件日志中发现流程模型，并通过可视化理解流程结构与依赖关系。同时讨论了各方法在捕捉底层流程动态方面的优劣。研究结果揭示了交通罚单管理流程的效率与有效性，为流程优化与决策提供重要参考。本研究展示了pm4py在支持流程挖掘任务中的实用性及其在分析真实业务流程中的潜力。
原文摘要 · Abstract (English)
Process-mining techniques have emerged as powerful tools for analyzing event data to gain insights into business processes. In this paper, we present a comprehensive analysis of road traffic fine management processes using the pm4py library in Python. We start by importing an event log dataset and explore its characteristics, including the distribution of activities and process variants. Through filtering and statistical analysis, we uncover key patterns and variations in the process executions. Subsequently, we apply various process-mining algorithms, including the Alpha Miner, Inductive Miner, and Heuristic Miner, to discover process models from the event log data. We visualize the discovered models to understand the workflow structures and dependencies within the process. Additionally, we discuss the strengths and limitations of each mining approach in capturing the underlying process dynamics. Our findings shed light on the efficiency and effectiveness of road traffic fine management processes, providing valuable insights for process optimization and decision-making. This study demonstrates the utility of pm4py in facilitating process mining tasks and its potential for analyzing real-world business processes.
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