Simplified Process Model Discovery Based on Role-Oriented Genetic Mining

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@Article{oai:pubmedcentral.nih.gov:3926309,

• author = "Weidong Zhao and Xi Liu and Weihui Dai",
• title = "Simplified Process Model Discovery Based on Role-Oriented Genetic Mining",
• journal = "The Scientific World Journal",
• year = "2014",
• month = jan # "~29",
• pages = "Article ID 298592",
• keywords = "genetic algorithms, genetic programming",
• publisher = "Hindawi Publishing Corporation",
• bibsource = "OAI-PMH server at www.ncbi.nlm.nih.gov",
• language = "en",
• oai = "oai:pubmedcentral.nih.gov:3926309",
• rights = "Copyright 2014 Weidong Zhao et al.; This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.",
• URL = "http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3926309",
• URL = "http://www.ncbi.nlm.nih.gov/pubmed/24616618",
• DOI = "doi:10.1155/2014/298592",
• abstract = "Process mining is automated acquisition of process models from event logs. Although many process mining techniques have been developed, most of them are based on control flow. Meanwhile, the existing role-oriented process mining methods focus on correctness and integrity of roles while ignoring role complexity of the process model, which directly impacts understandability and quality of the model. To address these problems, we propose a genetic programming approach to mine the simplified process model. Using a new metric of process complexity in terms of roles as the fitness function, we can find simpler process models. The new role complexity metric of process models is designed from role cohesion and coupling, and applied to discover roles in process models. Moreover, the higher fitness derived from role complexity metric also provides a guideline for redesigning process models. Finally, we conduct case study and experiments to show that the proposed method is more effective for streamlining the process by comparing with related studies.",
• notes = "Software School, Fudan University, No. 220 Handan Road, Shanghai 200433, China",

}

Genetic Programming entries for WeiDong Zhao Xi Liu Weihui Dai

Citations