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首页> 外文会议>2019 International Conference on Process Mining >Likelihood-based Multiple Imputation by Event Chain Methodology for Repair of Imperfect Event Logs with Missing Data
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Likelihood-based Multiple Imputation by Event Chain Methodology for Repair of Imperfect Event Logs with Missing Data

机译：基于事件链方法的基于可能性的多重插补，用于修复缺失数据的不完善事件日志

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摘要

The event log recorded through an information system may be missing for various reasons, which fact may result in an imperfect event log. Performing analyses using such an imperfect event log can seriously affect the quality of the obtained results. Therefore, analyses should be performed only after processing of the missing part in the imperfect event log. In the fields of data mining and statistical analysis, various methodologies have been developed to handle data with missing values, but there are not many studies dealing with incomplete event logs that have missing data in the field of process mining. In this paper, we propose a likelihood-based Multiple Imputation by Event Chain (MIEC) method for dealing with imperfect event logs with missing data. An experiment was performed using sample event logs, and a case study was conducted using a real steel manufacturing event log to verify our method. We expect the proposed method to repair the imperfect event log to a high level and to obtain analysis result with high quality even if there are many missing data.
机译：由于各种原因，可能会丢失通过信息系统记录的事件日志，这可能会导致不完整的事件日志。使用这种不完善的事件日志执行分析会严重影响所获得结果的质量。因此，仅应在处理不完善事件日志中的缺失部分之后执行分析。在数据挖掘和统计分析领域，已经开发了各种方法来处理具有缺失值的数据，但是在过程挖掘领域中，针对具有缺失数据的不完整事件日志的研究并不多。在本文中，我们提出了一种基于可能性的事件链多重插补（MIEC）方法，用于处理缺失数据的不完善事件日志。使用样本事件日志进行了实验，并使用真实的钢铁制造事件日志进行了案例研究以验证我们的方法。我们期望所提出的方法能够将不完善的事件日志修复到较高水平，即使丢失了许多数据，也可以获得高质量的分析结果。

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