Computer Science

Computer Science ›› 2019, Vol. 46 ›› Issue (12): 334-340.doi: 10.11896/jsjkx.180901654

• Interdiscipline & Frontier • Previous Articles    

Method of Mining Hidden Transition of Business Process Based on Behavior Profiles

SONG Jian, FANG Xian-wen, WANG Li-li, LIU Xiang-wei   

  1. (College of Mechanics and Optoelectronics Physics,Anhui University of Science and Technology,Huainan,Anhui 232001,China)
  • Received:2018-09-05 Online:2019-12-15 Published:2019-12-17

PDF (PC)

642

Abstract: In the process of business process optimization,mining hidden transitions from infrequent behaviors is one of the important tasks.Mining the hidden transitions from infrequent behaviors can better restore the process model and improve the efficiency of the process.Based on the theory of behavioral profiles,this paper mined logs from relatively high frequency and obtained initial models.Firstly,the event log is filtered by the reasonableness threshold to obtain a valid low-frequency sequence log.Then,the low-frequency sequence log is used to optimize the initial model,and the behavioral profiles relationship between each activity is compared with the source model to find the changed region,and the possible hidden transitions are minned.Next,through the optimization of the indicators to further verify the hidden transitions,a complete and accurate model of the implicit transition process is obtained.Finally,the model is analyzed by concrete examples and simulations to verify the effectiveness of the method.

Key words: Behavioral profiles, Hide transition, Petri net, Process model

CLC Number: 

  • TP391.9
[1]WEIJTERS A J M M,VAN DER AALST W M P,DE MEDEIROS A K A.Process mining with the heuristics miner-algorithm[R].Technische Universiteit Eindhoven,Technical report WP 166,2006:1-34.
[2]CONFORTI R,ROSA M L,HOFSTEDE A H M.Filtering out infrequent behavior from business process event logs[J].IEEE Transactions on Knowledge and Data Engineering,2016,29:300-314.
[3]VAN DER AALST W M P.Process Mining Discovery,Con- formance and Enhancement of Business Processes[M].Berlin:Springer-Verlag,2011:191-211.
[4]BUIJS J C A M,VAN DONGEN B F,VAN DER AALST W M P.A genetic algorithm for discovering process trees[C]//Proceedings of the 2012 IEEE Congress on Evolutionary Computation (CEC),IEEE,2012:1-8.
[5] LEEMANS S J J,FAHLAND D,AALST W M P V D.Using Life Cycle Information in Process Discovery[M]//Business Process Management Workshops.2016:1-12.
[6] AALST W M P V D,KALENKOVA A,RUBIN V,et al. Process Discovery Using Localized Events [M].Application and Theory of Petri Nets and Concurrency.Springer International Publishing,2015:287-308.
[7]NIEK T,BENJAMIN D,NATALIA S,et al.Interest-Driven Discovery of Local Process Models[J].Information Systems,2018:1-16.arXiv:1703.07116.
[8]ROZINAT A,W M PVAN DER AALST.Conformance Tes- ting:Measuring the Fit and Appropriateness of Event Logs and Process Models[J].Computer Science,2005,3812 LNCS:163-176.
[9]吴哲辉.Petri网理论[M].北京:机械工业出版社,2006:6-22.
[10]FANG X W,WU J Z,LIU X W.An Optimized Method of Business Process Mining Based on the Behavior Profile of Petri Net[J].Information Technology Journal,2014,13(1):86-93.
[11]WEIDLICH M,WESKE M,MENDLING J.Change propagation in process models using behavioural profiles[C]//2009 IEEE International Conference on Services Computing.Bangalore,India,2009:32-40.
[12]VAN DER AALST W,WEIJTERS T,MARUSTER L.Workflow mining:Discovering process models from event logs[J].IEEE Transactions on Knowledge and Data Engineering,2004,16(9):1128-1142.
[13]CHENG H J,KUMARA.Process Mining on Noisy Logs－Can log sanitization help to improve performance?[J].Decision Support Systems,2015,79(C):138-149.
[14]KUNZE M,WEIDLICH M,WESKE M.Querying process models by behavior inclusion[J].Software & Systems Modeling,2015,14(3),1105-1125.
[15]CHAPELA-CAMPA D,MUCIENTES M,LAMA M.Discove- ring Infrequent Behavioral Patterns in Process Models[C]//International Conference on Business Process Management.Springer,Cham,2017:324-340.
[1] LI Qing, LIU Wei, GUAN Meng-zhen, DU Yu-yue, SUN Hong-wei. Modeling and Analysis of Emergency Decision Making Based on Logical Probability GamePetri Net [J]. Computer Science, 2022, 49(4): 294-301.
[2] TAO Xiao-yan, YAN Chun-gang, LIU Guan-jun. Dynamic Data Refining Strategy for Soundness Verification Based on WFT-net [J]. Computer Science, 2021, 48(7): 99-104.
[3] LAI Xiang-wei, ZHENG Wan-bo, WU Yan-qing, XIA Yun-ni, RAN Qi-hua, DONG Yin-huan. Task Collaborative Process Network Model and Time Analysis of Mine Accident Emergency Rescue Digital Plan [J]. Computer Science, 2021, 48(6A): 596-602.
[4] NING Yu-hui, YAO Xi. Design and Implementation of Emergency Command System [J]. Computer Science, 2021, 48(6A): 613-618.
[5] WANG Wu-song, FANG Huan, ZHENG Xue-wen. Process Variants Merging Method Based on Group-fair Control Flow Structure [J]. Computer Science, 2021, 48(12): 170-180.
[6] 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.
[7] SUN Shu-ya, FANG Huan, FANG Xian-wen. Log-induced Morphological Fragments Process Clustering Method [J]. Computer Science, 2019, 46(8): 71-77.
[8] SONG Jian,FANG Xian-wen,WANG Li-li. Process Model Mining Method Based on Process Cut [J]. Computer Science, 2019, 46(7): 315-321.
[9] 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.
[10] YING Li, FANG Xian-wen, WANG Li-li, LIU Xiang-wei. Change Region Analysis of Configurable Business Process Model Based on Business Capability [J]. Computer Science, 2019, 46(10): 322-328.
[11] 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.
[12] YIN Liang,HE Ming-li,XIE Wen-bo,CHEN Duan-bing. Process Modeling on Knowledge Graph of Equipment and Standard [J]. Computer Science, 2018, 45(6A): 502-505.
[13] 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.
[14] 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.
[15] ZHAI Peng-jun, FANG Xian-wen and LIU Xiang-wei. Interaction Process Model Mining Method Based on Interface Transitions [J]. Computer Science, 2018, 45(3): 317-321.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.