基于结构和行为语义的子流程发现方法

doi:10.11896/jsjkx.210100089

计算机科学 ›› 2021, Vol. 48 ›› Issue (11A): 659-665.doi: 10.11896/jsjkx.210100089

基于结构和行为语义的子流程发现方法

孙善武, 王楠

吉林财经大学管理科学与信息工程学院长春130117

出版日期:2021-11-10 发布日期:2021-11-12
通讯作者: 王楠(ctuwangnan@126.com)
作者简介:ctusunshanwu@126.com
基金资助:
吉林省教育厅“十三五”社会科学研究项目(JJKH20200156SK);吉林省教育科学“十三五”规划基金项目(ZD20024);吉林省教育厅“十三五”科学技术研究项目(JJKH20210131KJ);国家自然科学基金项目(61702213)

Subprocesses Discovery Based on Structure and Activity Semantics

SUN Shan-wu, WANG Nan

College of Management Science and Information Engineering,Jilin University of Finance and Economics,Changchun 130117,China

Online:2021-11-10 Published:2021-11-12
About author:SUN Shan-wu,born in 1969,associate professor.His main research interests include machine learning business process modeling,and network security.
WANG Nan,born in 1980,Ph.D,associate professor,is a member of China Computer Federation.Her main research interests include machine lear-ning,public opinion analysis and BPMA.
Supported by:
13th Five Year Plan Social Science Research Project of Jilin Provincial Department of Education(JJKH20200156SK),13th Five Year Plan of Educational Science in Jilin Province(ZD20024),13th Five Year Plan Science Research Project of Jilin Provincial Department of Education(JJKH20210131KJ) and National Natural Science Foundation of China(61702213).

摘要/Abstract

摘要： 越来越多的企业以流程模型的形式进行业务管理,并且要求从不同的细节层描述相同的流程。业务流程模型抽象技术对于给定的细节流程模型,生成该模型的抽象表示,从而达到特定的抽象目标。构造流程的“概要视图”以加速对复杂流程的理解是流程抽象的一个需求最显著的用例,其中一个关键问题是将初始模型中的细节行为集合转换成抽象模型中的粗粒度的行为(子流程)。很多学者对流程抽象方法进行了研究,其中以基于结构的抽象方法居多。结构抽象方法中,初始模型中待抽象的行为集合仅仅根据控制流关系得到,没有考虑到行为的域语义,生成了大量的业务逻辑性不完整的待抽象的流程片段作为候选子流程。在流程的控制流结构基础上,引入行为语义信息,自底向上对流程结构树中每层的标准部件进行语义扩展,将标准部件与其相邻的兄弟节点之间的语义进行相似性评估,发现与标准部件语义描述最相似的行为集合,并有选择地聚合得到最接近包含完整业务含义的流程片段作为候选子流程。文章利用真实的流程案例对基于结构的抽象方法(将流程结构树中每个标准部件作为候选子流程)和提出的子流程发现方法做实验对比分析,提出的方法大大减少了不相关候选子流程的数量,生成的待抽象流程片段更加接近人工设计的子流程。

关键词: 流程结构树, 流程行为语义, 模型抽象, 行为相似性, 子流程发现

Abstract: More companies document their business operations in the form of process models,and require descriptions of one process on various levels of detail.Given a detailed process model,business process model abstraction (BPMA) delivers abstract representations for the same process to achieve particular abstraction goals.A prominent BPMA use case is a construction of a process “quick view” for rapidly comprehending a complex process.A key problem in this abstraction scenario is the transition from detailed activities in the initial model to coarse-grained activities.Many researchers have studied the methods of process abstraction most of which are based on structure.The structure-based abstraction derives the set of activities to be abstracted from the original model according only to control flow relations,but not considering the domain semantics,resulting in many process fragments (candidate subprocess) with incomplete business logicality.This paper bases on the structure of process and activity semantics to extend every canonical component of the process structure tree from the bottom up.This paper assesses the similarity between the canonical component and all its adjacent nodes to find out the group of activities which is the most similar to the canonical component.And then it aggregates the generated group of activities to derive the process segments as candidate subprocesses.This paper uses real-running process cases to compare structure-based abstraction method which takes every canonical component as a candidate subprocess with the proposed method.The results show that the number of the subprocesses with uncompleted business meaning is greatly reduced and the generated groups of activities are more similar to manually designed subprocesses.

Key words: Activities similarity, Model abstraction, Process activity semantics, Process structure tree, Subprocess discovery

中图分类号:

TP391

孙善武, 王楠. 基于结构和行为语义的子流程发现方法[J]. 计算机科学, 2021, 48(11A): 659-665. https://doi.org/10.11896/jsjkx.210100089

SUN Shan-wu, WANG Nan. Subprocesses Discovery Based on Structure and Activity Semantics[J]. Computer Science, 2021, 48(11A): 659-665. https://doi.org/10.11896/jsjkx.210100089

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基于结构和行为语义的子流程发现方法

Subprocesses Discovery Based on Structure and Activity Semantics

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