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
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