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, Petri net, Process clustering, Process combination

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.
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