Filtering Infrequent Behavior in Business Process Discovery by Using the Minimum Expectation

Filtering Infrequent Behavior in Business Process Discovery by Using the Minimum Expectation

Ying Huang (Gannan Normal University, Ganzhou, China), Liyun Zhong (Gannan Normal University, Ganzhou, China) and Yan Chen (South China Agricultural University, Guangzhou, China)
DOI: 10.4018/IJCINI.2020040101


The aim of process discovery is to discover process models from the process execution data stored in event logs. In the era of “Big Data,” one of the key challenges is to analyze the large amounts of collected data in meaningful and scalable ways. Most process discovery algorithms assume that all the data in an event log fully comply with the process execution specification, and the process event logs are no exception. However, real event logs contain large amounts of noise and data from irrelevant infrequent behavior. The infrequent behavior or noise has a negative influence on the process discovery procedure. This article presents a technique to remove infrequent behavior from event logs by calculating the minimum expectation of the process event log. The method was evaluated in detail, and the results showed that its application in existing process discovery algorithms significantly improves the quality of the discovered process models and that it scales well to large datasets.
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A number of outlier detection algorithms have been proposed in the data mining field. These algorithms build a data model (e.g., a statistical, linear, or probabilistic model) that describes the normal behavior and considers all data points that deviate from this model as outliers (Aggarwal et al., 2015).

In the context of temporal data, these algorithms have been extensively surveyed by Gupta et al. (2014) (for events with continuous values, known as time series) and by Chandola et al. (2012) (for events with discrete values, known as discrete sequences).

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