Simplified Process Model Discovery Based on Role-Oriented Genetic Mining

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@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