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| 一种并行化的启发式流程挖掘算法 |
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鲁法明1,2, 曾庆田3,1, 段华4, 程久军2, 包云霞4
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1.山东科技大学 信息科学与工程学院, 山东 青岛 266590;2.嵌入式系统与服务计算教育部重点实验室同济大学, 上海 200092;3.山东科技大学 电子通信与物理学院, 山东 青岛 266590;4.山东科技大学 数学与系统科学学院, 山东 青岛 266590
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| 摘要: |
| 启发式流程挖掘算法在日志噪音与不完备日志的处理方面优势显著,但是现有算法对长距离依赖关系以及2-循环特殊结构的处理存在不足,而且算法未进行并行化处理.针对上述问题,基于执行任务集将流程模型划分为多个案例模型,结合改进的启发式算法并行挖掘各个案例模型所对应的C-net模型;再将上述模型集成得到完整流程对应的C-net.同时,将长距离依赖关系扩展为决策点处两个任务子集之间的非局部依赖关系,给出了更为准确的长距离依赖关系度量指标和挖掘算法.上述改进措施使得该算法更为精确、高效. |
| 关键词: 流程挖掘 启发式挖掘算法 长距离依赖关系 案例模型 案例簇 |
| DOI：10.13328/j.cnki.jos.004769 |
| 分类号: |
| 基金项目:国家自然科学基金(61170079, 61202152, 61472229, 61472284); 山东省科技发展项目(2014GGX101035); 山东省优秀中青年科学家科研奖励基金(BS2014DX013); 青岛市科技计划基础研究项目(13-1-4-153-jch, 2013-1-24); 同济大学嵌入式系统与服务计算教育部重点实验室开放课题基金(ESSCKF201403); 山东科技大学群星计划(qx2013113, qx2013354) |
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| Parallelized Heuristic Process Mining Algorithm |
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LU Fa-Ming1,2, ZENG Qing-Tian3,1, DUAN Hua4, CHENG Jiu-Jun2, BAO Yun-Xia4
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1.College of Information Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China;2.The Key Laboratory of Embedded System and Service Computing, Ministry of Education Tongji University, Shanghai 200092, China;3.College of Electronic Communication and Physics, Shandong University of Science and Technology, Qingdao 266590, China;4.College of Mathematics and System Science, Shandong University of Science and Technology, Qingdao 266590, China
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| Abstract: |
| Heuristic process mining algorithm has a significant advantage in dealing with noise and incomplete logs. However, existing heuristic process mining algorithms cannot handle long-distance dependencies and lenth-2-loop structures correctly in some special situations. Besides, none of them are parallelized. To address the problems, process models are divided into multiple case models according to executed activity set at first. Then the C-nets corresponding to case models are discovered with an improved heuristic process mining algorithm in parallel. After that, these C-nets are integrated to derive the complete process model. Meanwhile, the definition of long- distance dependencies is extended to non-local dependencies between two activity sets in decision points. In addition, a more accurate long- distance dependency metrics and its corresponding mining algorithm are presented. These improvements make the proposed algorithm more accurate and efficient. |
| Key words: process mining heuristic mining algorithm long distance dependency case model case cluster |
