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題名：發展整合性演化式方法於無雜訊與有雜訊事件記錄檔之流程探勘適應值改善
作者：鄭歆蓉
作者(外文)：Hsin-Jung Cheng
校院名稱：國立臺灣科技大學
系所名稱：工業管理系
指導教授：歐陽超
學位類別：博士
出版日期：2015
主題關鍵詞：流程探勘；平行結構；流程模型；基因演算法；粒子群演算法；差分演化演算法；模擬退火法；雜訊；適應值；演化式方法；Process mining；Parallel structures；Process model；Genetic algorithm；Particle swarm optimization；Differential evolution；Simulated annealing；Noisy log；Fitness；Evolutionary algorithm
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隨著資訊科技的快速變遷，企業活動的發展也日趨繁複，當企業發展到一定規模時，其企業流程定有相當的複雜程度，欲了解龐雜的企業流程內所隱含之各項資訊，往往不是件容易的事情。企業流程模型是一種讓企業內部所有商業活動或行為能一目了然的模型。透過企業流程模型，企業能以較簡易的方式得知現有流程之情形。流程探勘是一種能探勘出企業流程模型之技術，其目的是從事件記錄檔中探勘出可以表現出記錄在事件記錄檔中的流程行為的模型。一個具有高適應值的探勘流程模型可以反映出大部分記錄在事件記錄檔中的流程行為。然而，事件記錄檔中可能會含有不完整或錯誤的流程資料。因此，本論文旨在討論完整與有雜訊的事件記錄檔之流程探勘問題。
本論文首先探討在完整的事件記錄檔的流程探勘問題。在一般企業流程模型中，可能同時存在多種流程結構，例如：平行結構、選擇結構、非自由選擇結構、迴圈結構…等。然而，平行結構是最難探勘的部分。因此，本論文發展一個基於基因流程探勘法、粒子群演算法與差分演化演算法之整合式演化式方法，目的是從含有複合平行結構的完整的事件記錄檔中找出具有高適應值之流程模型。實驗結果顯示，本論文提出之整合式演化式方法可以從含有複合平行結構之事件記錄檔中有效地探勘出具有高適應值之流程模型，並可利用探勘出之流程模型進行持續性之流程改善。
接著，本論文探討在有雜訊的事件記錄檔的流程探勘問題。有雜訊的事件記錄檔是指事件記錄是不完整或有錯誤在內。因此，本論文發展一個整合粒子群演算法與模擬退火法之演化式方法，透過粒子群演算法與模擬退火法能降低雜訊干擾的特性，從含有雜訊之事件記錄檔中探勘出具有高適應值之流程模型。經實驗證明，本論文提出之整合粒子群演算法與模擬退火法之演化式方法，較現有最好之流程探勘法能更有效率地從含有雜訊之事件記錄檔中探勘出具有高適應值之流程模型。
With the changes of information technology, the complexity of the development of business activities has increased markedly. To understand the connotative information of complex business processes is difficult. A process model is a way that can describe the internal activities or behavior in businesses. Process mining (PM) is a technique to extract a process model from an event log to represent the process behavior recorded in that event log. A mined process model with high fitness means that it can reflect most of the process behavior recorded in the event log. Previous studies have shown that the mined model with high fitness can be used in process improvement, such as fraud detection, continuous process improvement, and benchmarking. Additionally, event logs may contain incomplete or incorrect process data. Such logs are called noisy logs. Therefore, this dissertation aims to study the discovery of process models with high fitness for noise-free and noisy event logs.
Firstly, this dissertation considers discovering parallel (AND) structures in PM. There are several problematic structures in PM, including parallel (AND) structures, exclusive-choice (XOR) structures, non-free-choice structures, loops, and noise. Some PM approaches have been conducted to address only one or a few problematic structures. Genetic process mining (GPM) is a well-known PM method which can simultaneously handle most of the problematic structures. However, GPM still cannot effectively discover parallel structures from noise-free logs. This dissertation proposes a PM approach based on integration of GPM, particle swarm optimization (PSO), and differential evolution (DE) to find process models with high fitness for noise-free logs involving multiple parallel structures. The results show that the proposed approach does indeed lead to improvement in gaining process models with high fitness for event logs involving multiple parallel structures.
Secondly, this dissertation considers discovering process models from noisy logs. In some industries (such as hospitals), data must sometimes be manually recorded and data concerning some tasks that are performed during emergency situations may be missing. Manual logging may result in logging errors. Missing events result in incomplete logs. Therefore, event logs may contain incorrect process data. These logs are called noisy logs. This dissertation develops a PSOSA approach that combines particle swarm optimization (PSO) with simulated annealing (SA) to discover process models with high fitness from noisy logs. The results achieved using this PSOSA approach reveal that it improves fitness of process models found using noisy logs.
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