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Process mining method based on genetic technology research

Author: LingYong
Tutor: ZhangLiQun
School: Shandong University
Course: Computer Software and Theory
Keywords: Process mining Event Log Genetic Algorithms Cause and Effect Matrix Fitness function Genetic Operators
CLC: TP311.13
Type: Master's thesis
Year: 2009
Downloads: 91
Quote: 2
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Abstract


A workflow is a reflection of a computerized model of business processes, in order to support the implementation of advanced computer environment business process integration and business process automation and the establishment, by the workflow management system to perform the business model. Workflow lifecycle including workflow design, workflow configuration, workflow execution, workflow diagnosed four stages. Workflow mining technology is not a workflow design tool, but its existing business processes to fully understand the implementation of great help. Workflow mining objectives are: reversal process to collect and use performance data to support workflow design and analysis. This paper first introduces the latest workflow technology development, as well as WfMC workflow reference model, then article summarizes the current main methods of workflow modeling, workflow modeling presented challenges and opportunities exist in the field. Process mining methods discussed in detail before, the article with mining-related technologies and theories are described, including the log mathematical expression model, Petri nets and related properties of workflow nets, Petri nets to workflow net mapping relationships. Then article describes the current process mining areas more perfect α-algorithm, pointing out α-algorithm mining certain structural deficiencies and limitations. In this paper, existing process mining algorithm deficiencies and defects, and in view of the genetic algorithm is adaptive, global optimization, implicit parallelism and simple forms and other characteristics, proposed the use of genetic approaches for process mining. In introducing the use of genetic methods to mining process model, we define: Internal description form of fitness function, genetic operators. Internal formal definition of the genetic algorithm described in the search space, able to support the process model, in addition to all the usual structure of repetitive tasks (including sequential, parallel, choice, circulation, non-free choice, invisible tasks); fitness function according to the event log The correct evaluation of the process model has been created (ie genetic individual) fitness; genetic operators to ensure coverage described in the form of the internal space defined global search for all the points. They will be asked to do a genetic algorithm theoretical preparation. Finally, we propose a mining process based on genetic algorithm, the algorithm contains a number of genetic individuals from the initial population began. Each corresponds to a process model of genetic individuals, and use the fitness function to record its pros and cons of being able to evaluate genetic individual's ability to reproduce the event log. Through genetic fitness function to reflect the individual degree of fit with the event log, while using recombinant genetic individual genetic operators to generate new process model. Finally, the log by running the simulation to obtain sufficient experimental data, the quality of the algorithm was tested excavation, it is proved that the algorithm has obvious advantages mining process, an effective solution to the α-algorithm in mining certain structural deficiencies and limitations .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer software > Program design,software engineering > Programming > Database theory and systems
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