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首页> 外文会议>Conference on Fundamental and Applied Science for Advanced Technology >Mining Fuzzy Time Interval Sequential Pattern on Event Log Data using FP-Growth-Prefix-Span Algorithms
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Mining Fuzzy Time Interval Sequential Pattern on Event Log Data using FP-Growth-Prefix-Span Algorithms

机译：使用FP-Clue-Prefix-Span算法发生在事件日志数据上的挖掘模糊时间间隔顺序图案

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摘要

Rapid technological developments caused the increasing number of computerized data processing. With the increasing complexity of business processes, business process management technologies such as ERP (Enterprise Resource Planning) are increasingly being used. This resulted in the availability of data more abundant so that excavation and search information from the dataset will be a valuable knowledge. In this paper, we have done the process mining to obtain an interesting pattern of event log data. In this research, data mining method that we are used is the sequential pattern mining algorithm using FP-Growth-Prefix Span. In addition, we are also used the fuzzy approach to handle the time interval of the analyzed data, so that the sequential pattern that produced become fuzzy time-interval sequential pattern. The application of these methods in a business processes that produce fuzzy time interval sequential pattern. From the analysis, the result shown that there is a minimum effect on the pattern of the resulting support. Furthermore, the results of the analysis can be used as consideration in the analysis of business processes.
机译：快速技术发展导致越来越多的计算机化数据处理。随着业务流程的复杂性越来越复杂，越来越多地使用ERP（企业资源规划）等业务流程管理技术。这导致数据的可用性更加丰富，因此来自数据集的挖掘和搜索信息将是一个有价值的知识。在本文中，我们已经完成了过程挖掘以获得事件日志数据的有趣模式。在本研究中，我们使用的数据挖掘方法是使用FP-Grow-Prefix跨度的顺序模式挖掘算法。另外，我们也使用模糊方法来处理分析的数据的时间间隔，使得产生的顺序模式成为模糊时间间隔顺序图案。这些方法在生产模糊时间间隔顺序模式的业务流程中的应用。从分析中，结果表明对所得支持的模式有最小的影响。此外，分析结果可以在商业流程分析中被用作考虑因素。

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