Computer Science ›› 2019, Vol. 46 ›› Issue (11): 334-339.doi: 10.11896/jsjkx.180901710

• Interdiscipline & Frontier • Previous Articles    

Approach for Mining Block Structure Process from Complex Log Using Log Partitioning

DUAN Rui, FANG Huan, ZHAN Yue   

  1. (School of Mathematics and Big Data,Anhui University of Science and Technology,Huainan,Anhui 232001,China)
  • Received:2018-09-12 Online:2019-11-15 Published:2019-11-14

Abstract: With the development of enterprises,more and more logs are generated and recorded by the system,and the process of mining block structures from cumbersome and complicated logs becomes more challenging.This paper proposed an approach of vertically dividing logs,greatly reducing the number of instances of each log partition,and shorte-ning the length of each trace to process complex logs and mining accurate models from them.The basis of log division is activity division.Firstly,based on the idea of behavioral association,the concept of common transition is proposed torea-lize the aggregation division of interrelated activities.Then,from the perspective of the number of common transitions in the log,the activity set is divided by mutually different and interleaved methods,thereby realizing the division of mo-dules and logs.The proposed module and log partitioning method can be iterated until that the log partitioning is simple enough.Finally,a block structure is mined from each divided simple log,and a reasonable overall system model is formed by combining block structures.The feasibility of the proposed method is verified by Prom experiment.

Key words: Block structure, Common transition, Complex log, Log partitioning

CLC Number: 

  • O175
[1]AALST W V D,WEIJTERS T,MARUSTER L.WorkflowMining:Discovering Process Models from Event Logs[J].IEEE Transactions on Knowledge & Data Engineering,2004,16(9):1128-1142.
[2]WEN L,WANG J,SUN J.Mining invisible tasks from event logs[C]∥Joint,Asia-Pacific Web and,International Conference on Web-Age Information Management Conference on Advances in Data and Web Management.Springer-Verlag,2007:358-365.
[3]WEIJTERS A,AALST W V D.Process mining with the heuristics miner-algorithm[J].Eindhoven University of Technology,2006,166:1-34.
[4]WEIJTERS A J M M,RIBEIRO J T S.Flexible Heuristics Miner(FHM)[C]∥Computational Intelligence and Data Mining.IEEE,2011:310-317.
[5]VANDEN BROUCKE S K L M,VANTHIENEN J,BAESENS B.Declarative process discovery with evolutionary computing[C]∥Evolutionary Computation.IEEE,2014:2412-2419.
[6]LEEMANS S J J,FAHLAND D,AALST W M P V D.Scalable process discovery and conformance checking[J].Software & Systems Modeling,2018,17(2):599-631.
[7]LEEMANS S J J,FAHLAND D,AALST W M P V D.Discovering Block-Structured Process Models from Incomplete Event Logs[C]∥International Conference on Applications and Theory of Petri Nets and Concurrency.Springer International Publi-shing,2014:91-110.
[8]LEEMANS S J J,FAHLAND D,AALST W M P V D.Discovering block-structured process models from event logs—a constructive approach[C]∥International Conference on Application and Theory of Petri Nets and Concurrency.Springer-Verlag,2013:311-329.
[9]BOUSHABA S,KABBAJ M I,BAKKOURY Z.Process discovery:Automated approach for block discovery[C]∥International Conference on Evaluation of Novel Approaches To Software Engineering.IEEE,2015:1-8.
[10]TAX N,SIDOROVA N,HAAKMA R,et al.Mining localprocess models[J].Journal of Innovation in Digital Ecosystems,2016,3(2):183-196.
[11]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.
[12]TAX N,ALASGAROV E,SIDOROVA N,et al.Time-BasedLabel Refinements to Discover More Precise Process Models[J].Journal of Ambient Intelligence and Smart Environments,2017,11(2):1001-1015.
[13]TAX N,SIDOROVA N,AALST W M P V D.Discovering more precise process models from event logs by filtering out chaotic activities[J].Journal of Intelligent Information Systems,2019,52(1):107-139.
[14]VAN DONGEN B F,DE MEDEIROS A K A,VERBEEK H M W,et al.The ProM Framework:A New Era in Process Mining Tool Support[C]∥International Conference on Applications and Theory of Petri Nets.Springer-Verlag,2005:444-454.
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