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Improved behavior model based on sequential rule mining
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DOI：10.1016/j.asoc.2018.01.035.png)
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Abstract
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The fourth industrial revolution leads the manufacturing companies to develop future and smart factories by merging automation and digitalization to result a more efficient production method. An evolutionary and competitive experimental approach is necessary to foster the innovation and the rapid change of the automation and digitalization. Consequently, software becomes an important component of industrial automation. One of the major challenge in Industry 4.0 is to industrialize the production of software. Software factory, as one industry with a virtual production line to produce software for manufacturing companies, offers a form of flexible employment, called as telecommuting work. Although this form brings many benefits for both employee and employers, some risks associated with telecommuting work exist. Monitoring the employee behavior is one of the employer way to see the accountability of the employee. Hence, understanding the human behavior during the production process would be an important issue for fulfilling overall operational excellence in software factory. Among approaches proposed to discover the human behavior based on the sequence activities, process mining is one of which has received attentions lately. While most recent process mining approaches in the domain of human behavior address process discovery and post-analysis, few of them have paid attentions on pre-analysis. The pre-analysis is one of the ways to produce a reliable and high-quality of event log which purposely impacts on discovering a daily common behavior and disregarding irregular sequential behavior. This study aims to propose a new way of pre-analysis using sequential rule mining. The key contributions of this research first, is to determine the potential local behaviors using sequential rule mining considering time constraint. Second, the local behaviors are used to enhance event log for discovering relevant behavior model. Third, the mined model is verified by performing conformance checking approach to check the conformity between the behavior model and the real logs based on three measurements: f-measure, ABA, and DMF. The resulting local behaviors, called as rules, can be used for guiding stakeholders to pinpoint the relevant behavior for human capital and productivity enhancement. (C) 2018 Elsevier B.V. All rights reserved.
Keywords:
Relevant behavior model
Log filtering
Process mining
Sequential rule mining
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