基于后继关系的行为块过程挖掘方法
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摘要
鉴于已有的过程挖掘方法在发现循环结构和隐藏行为上具有一定的局限性,提出一种基于后继关系的行为块挖掘算法。依据日志建立后继关系矩阵,分析了矩阵中变迁间对应的值,从而可以发现所有最小行为块和隐藏的行为关系,包括循环行为块;利用组合原理对带有重复变迁的最小行为块进行组合,得到结构行为块;组合所有行为块得到初始模型,利用已发现的隐藏行为关系修正初始模型得到更加精确的过程模型。通过实例分析和ProM的仿真实验验证了所提方法的可行性。
Aiming at the limitations of the existing process mining method in discovering cyclic structures and hidden behaviors,a mining algorithm based on successor relations was proposed.A successor relations matrix was established based on the logs,the corresponding values in the matrix of the transitions were analyzed,and all the minimum behavior blocks and the hidden behavioral relationships were found,which including cyclic behavior blocks.The combination principle was used to combine the minimum behavior blocks with repeated transitions,and the behavior blocks structure was constructed.The initial process model were obtained by combining all behavior blocks,and the more precise process model through the model adjustment was obtained by discovered hidden behavior relationships.The feasibility of the proposed method was verified by case study and simulation experiment on the platform ProM.
Aiming at the limitations of the existing process mining method in discovering cyclic structures and hidden behaviors,a mining algorithm based on successor relations was proposed.A successor relations matrix was established based on the logs,the corresponding values in the matrix of the transitions were analyzed,and all the minimum behavior blocks and the hidden behavioral relationships were found,which including cyclic behavior blocks.The combination principle was used to combine the minimum behavior blocks with repeated transitions,and the behavior blocks structure was constructed.The initial process model were obtained by combining all behavior blocks,and the more precise process model through the model adjustment was obtained by discovered hidden behavior relationships.The feasibility of the proposed method was verified by case study and simulation experiment on the platform ProM.
引文
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[3]MARUSTER L,WEIJTERS A,VAN DER AALST W M P,et al.A rule-based approach for process discovery[J].Data Mining&Knowledge Discovery,2015,13(1):67-87.
[4]LEEMANS S J J,FAHLAND D,VAN DER AALST W MP.Scalable process discovery and conformance checking[J].Software&Systems Modeling,2018,17(2):1-33.
[5]WEIJTERS A,VAN DER AALST W M P.Process mining with the heuristics miner-algorithm[EB/OL].(2006-01-08)[2018-08-27].http://wwwis.win.tue.nl/~wvdaalst/old/publications/p314.pdf.
[6]WEIJTERS A T,RIBEIRO J J.Flexible heuristics miner(FHM)[C]//Proceedings of IEEE Conference on Computational Intelligence and Data Mining.Washington,D.C.,USA:IEEE,2011:310-317.
[7]LEEMANS S J J,FAHLAND D,VAN DER AALST W MP.Discovering block-structured process models from incomplete event logs[C]//Proceedings of International Conference on Applications and Theory of Petri Nets and Concurrency.Berlin,Germany:Springer-Verlag,2014:91-110.
[8]TAX N,SIDOROVA N,HAAKMA R,et al.Mining local process models[J].Journal of Innovation in Digital Ecosystems.2016,3(2):183-196.
[9]WEERDT J D,BROUCKE S V,VANTHIENEN J,et al.Active trace clustering for improved process discovery[J].IEEE Transactions on Knowledge&Data Engineering,2013,25(12):2708-2720.
[10]TAX N,ALASGAROV E,SIDOROVA N,et al.Timebased label refinements to discover more precise process models[J].Journal of Ambient Intelligence and Smart Environments,2017,11(2):1001-1015.
[11]TAX N,SIDOROVA N,VAN DER AALST W M P.Discovering more precise process models from event logs by filtering out chaotic activities[J].Journal of Intelligent Information Systems,2019,52(1):107-139.
[12]BOUSHABA S,KABBAJ M I,BAKKOURY Z.Process discovery:automated approach for block dis-covery[C]//Proceedings of the International Conference on Evaluation of Novel Approaches to Software Engineering(ENASE).Washington,D.C.,USA:IEEE,2014:1-8.
[13]DELEONI M,VAN DER AALST W M P,DEES M.A general process mining framework for correlating,predicting and clustering dynamic behavior based on event logs[J].Information Systems,2016,56:235-257.
[14]VAN DER AALST W M P.Decomposing Petri nets for process mining:ageneric approach[J].Distributed&Parallel Databases,2013,31(4):471-507.
[15]CARMONA J.Projection approaches to process mining using region-based techniques[J].Data Mining&Knowledge Discovery,2012,24(1):218-246.
[16]BUIJS J,DONGEN B V,VAN DER AALST W M P.A genetic algorithm for discovering process trees[C]//Proceedings of IEEE Congress on Evolutionary Computation.Washington,D.C.,USA:IEEE,2012:1-8.
[17]POLYVYANYY A,VANHATALO J,VLZER H.Simplified computation and generalization of the refined process structure tree[M]//Web Services and Formal Methods.Berlin,Germany:Springer-Verlag,2010:25-41.
[18]CHAPELA-CAMPA D,MUCIENTES M,LAMA M.Discovering infrequent behavioral patterns in process models[C]//Proceedings of the International Conference on Business Process Management.Berlin,Germany:Springer-Verlag,2017:324-340.
[19]CONFORTI R,ROSA M L,HOFSTEDE A H M T.Filtering out infrequent behavior from business process event logs[J].IEEE Transactions on Knowledge&Data Engineering,2017,29(2):300-314.
[20]VAN DONGEN B F,DE MEDEIROS A K A,VERBEEK HM W,et al.The ProM framework:a new era in process mining tool support[C]//Proceedings of the International Conference on Applications and Theory of Petri Nets.Berlin,Germany:Springer-Verlag,2005:444-454.