Computer Science ›› 2019, Vol. 46 ›› Issue (8): 71-77.doi: 10.11896/j.issn.1002-137X.2019.08.011

• Big Data & Data Science • Previous Articles     Next Articles

Log-induced Morphological Fragments Process Clustering Method

SUN Shu-ya, FANG Huan, FANG Xian-wen   

  1. (School of Mathematics and Big Data,Anhui University of Science & Technology,Huainan,Anhui 232001,China)
  • Received:2018-08-15 Online:2019-08-15 Published:2019-08-15

Abstract: In the business process management system,there may be many different arrangements of several event sets in the task flow for performing the same purpose.Corresponding to the logs,it shows that many logs have many changes,but also have some common characteristics of many services.Therefore,extracting the commonality of the logs behavior and clustering multiple similar logs of the similar type of business system have positive significance for the business integration of similar processes.This paper proposed an approach of process clustering method.Firstly,low-frequency events are filtered out,and common high-frequency fragments from the morphological fragments in the log are extracted by automata.And then the extracted public high-frequency fragments are converted into clusters of similar logs through automation formal method.Then,a morphological fragment-based approach is proposed.A business combination algorithm is generated for those frequent execution paths of the commonality of the process model.By combining similar equivalent morphological fragments for business combination,a fused Petri net model is obtained.Finally,a practical case is proposed to verify the feasibility and validity of the proposed method

Key words: Morphological fragments, Process clustering, Process combination, Petri net

CLC Number: 

  • TP391.9
[1] AALST W M P V D.Process Mining - Data Science in Action(2nd edn)[M]. Springer,Heidelberg,2016.
[2] LIU X,DING C.Learning Workflow Models from Event Logs Using Co-clustering[J].International Journal of Web Services Research,2013,10(3):42-59.
[3] LEONI M D,AALST W M P V D,DEES M.A general process mining framework for correlating,predicting and clustering dynamic behavior based on event logs[J].Information Systems,2016,56(C):235-257.
[4] MILANI F,DUMAS M,AHMED N,et al.Modelling families of business process variants:A decomposition driven method[J].Information Systems,2016,56:55-72.
[5] LI C,REICHERT M,WOMBACHER A.Discovering process reference models from process variants using clustering techniques[J].Centre for Telematics & Information Technology University of Twente,2018,16(5):1-30.
[6] WESKE M.Business Process Management:Concepts,Langua- ges,Architectures[M].Springer-Verlag New York,Inc.2007.
[7] POURMASOUMI A,KAHANI M,BAGHERI E.Mining variable fragments from process event logs[J].Information Systems Frontiers,2017,19(6):1-21.
[8] MA H,TANG Y,WU L K.Model update method in process in- cremental mining[J].Computer Science,2009,36(5):154-157.
[9] BOLT A,LEONI M D,AALST W M P V D.Process Variant Comparison:Using Event Logs to Detect Differences in Behavior and Business Rules[J].Information Systems Frontiers,2018,74(1):53-66.
[10] DÖHRING M,REIJERS H A,SMIRNOV S.Configuration vs.adaptation for business process variant maintenance:An empirical study[J].Information Systems,2014,39(1):108-133.
[11] BUIJS J C A M,REIJERS H A.Comparing Business Process Variants Using Models and Event Logs[M]∥Enterprise,Business-Process and Information Systems Modeling.Springer Berlin Heidelberg,2014:154-168.
[12] BUIJS J,DONGEN B,AALST W.Mining Configurable Process Models from Collections of Event Logs[C]∥International Conference on Business Process Management.Springer-Verlag,2013:33-48.
[13] ASSY N,GAALOUL W,DEFUDE B.Mining Configurable Process Fragments for Business Process Design[M]∥Advancing the Impact of Design Science:Moving from Theory to Practice.Springer International Publishing,2014:209-224.
[14] HASANKIYADEH A,KAHANI M,BAGHERI E,et al.Mining common morphological fragments from process event logs[C]∥International Conference on Computer Science and Software Engineering.IBM Corp,2014:179-191.
[15] ASSY N,CHAN N,GAALOUL W,et al.Deriving configurable fragments for process design[J].International Journal of Business Process Integration & Management,2014,7(1):2-21.
[16] LU X X,FAHLAND D D,WIL V D A W.Interactively exploring logs and mining models with clustering,filtering,and relabeling[C]∥Proceedings of the BPM 2016 Tool Demonstration TRACK.2016.
[17] ASSY N,CHAN N,GAALOUL W.Assisting Business Process Design with Configurable Process Fragments[C]∥IEEE International Conference on Services Computing.IEEE Computer Society,2013:535-542.
[18] DERGUECH W,BHIRI S.Merging Business Process Variants [C]∥Business Information Systems,International Conference(Bis 2011).Poznan,Poland,DBLP,2011:86-97.
[19] 方贤文.Petri网行为轮廓理论及其应用[M].上海:上海交通大学出版社,2017:39-40.
[20] ZEMNI M A,HADJ-ALOUANE N B,MAMMAR A.Business Process Fragments Behavioral Merge[M]∥On the Move to Meaningful Internet Systems:OTM 2014 Conference.Berlin:Springer,2014:112-129.
[21] 蒋宗礼,姜守旭.形式语言与自动机理论[M].北京:清华大学出版社,2003:71-73.
[1] YANG Hao-ran, FANG Xian-wen. Business Process Consistency Analysis of Petri Net Based on Probability and Time Factor [J]. Computer Science, 2020, 47(5): 59-63.
[2] SU Qing,LIN Hao,HUANG Jian-feng,HE Fan,LIN Zhi-yi. Study on Dynamic-graph Watermarking Based on Petri Net Coding [J]. Computer Science, 2019, 46(7): 120-125.
[3] SONG Jian,FANG Xian-wen,WANG Li-li. Process Model Mining Method Based on Process Cut [J]. Computer Science, 2019, 46(7): 315-321.
[4] SONG Jian, FANG Xian-wen, WANG Li-li, LIU Xiang-wei. Method of Mining Hidden Transition of Business Process Based on Behavior Profiles [J]. Computer Science, 2019, 46(12): 334-340.
[5] CAO Rui, FANG Xian-wen, WANG Li-li. Method of Mining Conditional Infrequent Behavior Based on Communication Behavior Profile [J]. Computer Science, 2018, 45(8): 310-314.
[6] HE Lu-lu, FANG Huan. Change Propagation Method of Service-oriented Business Process Model with Data Flows Based on Petri Net [J]. Computer Science, 2018, 45(6A): 545-548, 567.
[7] ZHAO Pei-hai, WANG Mi-mi. Consistency Detction Method of Models Based on Three-dimensional Behavior Relation Graph [J]. Computer Science, 2018, 45(6): 156-160,165.
[8] GAO Ya-nan, FANG Xian-wen and WANG Li-li. Optimized Analysis of Business Process Configuration Based on Petri Net Behavior Closeness [J]. Computer Science, 2017, 44(Z6): 539-542.
[9] ZHOU Jie and LI Wen-jing. Research on Parallel Algorithm of Petri Net Based on Three-layer Mixed Programming Model [J]. Computer Science, 2017, 44(Z11): 586-591, 595.
[10] LIN Lei-lei, ZHOU Hua, DAI Fei, HE Zhen-li, SHEN Yong and KANG Hong-wei. Method of Software Architecture Refinement Based on Algebraic Semantics [J]. Computer Science, 2017, 44(7): 141-146.
[11] SONG Zhen-hua and ZHANG Guang-quan. Modeling of CPS Based on Aspect-oriented Spatial-Temporal Petri Net [J]. Computer Science, 2017, 44(7): 38-41, 73.
[12] SONG Xiang-jun and ZHANG Guang-quan. Modeling and Analysis of CPS Unmanned Vehicle Systems Based on Extended Hybrid Petri Net [J]. Computer Science, 2017, 44(7): 21-24.
[13] ZHAO Na, WANG Jian, LI Tong, YU Yong, LI Peng and XIE Zhong-wen. Study of Component Assembling Technologies under Object-oriented Trusted Component Model [J]. Computer Science, 2017, 44(11): 104-108.
[14] LI Xiang, LI Tong, XIE Zhong-wen, HE Yun, CHENG Lei and HAN Xu. Multi-layer Model for SaaS Multi-tenancy [J]. Computer Science, 2017, 44(11): 56-63.
[15] LIU Hong, LIU Xiang-wei and WANG Li-li. Business Process Model Optimized Analysis Method Based on Configuration Change [J]. Computer Science, 2016, 43(Z11): 509-512.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75, 88 .
[2] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[3] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[4] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[5] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99, 116 .
[6] ZHOU Yan-ping and YE Qiao-lin. L1-norm Distance Based Least Squares Twin Support Vector Machine[J]. Computer Science, 2018, 45(4): 100 -105, 130 .
[7] LIU Bo-yi, TANG Xiang-yan and CHENG Jie-ren. Recognition Method for Corn Borer Based on Templates Matching in Muliple Growth Periods[J]. Computer Science, 2018, 45(4): 106 -111, 142 .
[8] GENG Hai-jun, SHI Xin-gang, WANG Zhi-liang, YIN Xia and YIN Shao-ping. Energy-efficient Intra-domain Routing Algorithm Based on Directed Acyclic Graph[J]. Computer Science, 2018, 45(4): 112 -116 .
[9] CUI Qiong, LI Jian-hua, WANG Hong and NAN Ming-li. Resilience Analysis Model of Networked Command Information System Based on Node Repairability[J]. Computer Science, 2018, 45(4): 117 -121, 136 .
[10] WANG Zhen-chao, HOU Huan-huan and LIAN Rui. Path Optimization Scheme for Restraining Degree of Disorder in CMT[J]. Computer Science, 2018, 45(4): 122 -125 .