A repairing missing activities approach with succession relation for event logs

Liu, Jie; Xu, Jiuyun; Zhang, Ruru; Reiff-Marganiec, Stephan

doi:10.1007/s10115-020-01524-6

Cited by 6 publications

(16 citation statements)

References 35 publications

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“…This method does not rely on any prior knowledge of the business process that generates the event log and has shown significant performance in terms of activity labels and timestamps in artificial event logs. In PROELR [14] and SRBA [16], trace clustering methods are used to cluster complete traces, which are traces without any missing activity labels. Each incomplete trace, referring to traces with missing activity labels, is assigned to the nearest cluster.…”

Section: A Attribute-level Repairmentioning

confidence: 99%

“…Subsequently, the incomplete traces are repaired based on the characteristics of the corresponding trace clusters. It is worth noting that both [14] and [16] can only repair missing values at the individual activity level. MIEC [17] is a likelihood-based multiple imputation technique for repairing missing data in event logs.…”

Section: A Attribute-level Repairmentioning

confidence: 99%

“…Clustering is used to partition process logs into trace clusters, which helps reduce heterogeneity and improve understandability [16]. Trace clustering splits observed different behaviors into several groups of multiple sub-logs with similar behaviors.…”

Section: A Log Clusteringmentioning

confidence: 99%

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Log Clustering-based Method for Repairing Missing Traces with Context Probability Information

Fang,

2024

ijacsa

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In real business processes, low quality event logs due to outliers and missing values tend to degrade the performance of process mining related algorithms, which in turn affects the correct execution of decisions. In order to repair the missing values in event logs under the condition that the reference model of the process system is unknown, this paper proposes a method that can repair consecutive missing values. First, the event logs are divided according to the integrity of the trace, and then the cluster algorithm is applied to complete logs to generate homogeneous trace clusters. Then match the missing trace to the most similar sub log, generate candidate sequences according to the context of the missing part, calculate the context probability of each candidate sequence, and select the one with the highest probability as the repair result. When the number of missing items in the trace is 1, our method has the highest repair accuracy of 97.5 percent in the Small log and 93.3 percent in the real event logs bpic20. Finally, the feasibility of this method is verified on four event logs with different missing ratios and has certain advantages compared with existing methods.

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Section: A Attribute-level Repairmentioning

confidence: 99%

Section: A Attribute-level Repairmentioning

confidence: 99%

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Log Clustering-based Method for Repairing Missing Traces with Context Probability Information

Fang,

2024

ijacsa

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“…Trace clustering is mostly used to minimize event logs' volume, complexity, and granularity issues [26][27][28]. Some researchers utilized trace clustering to discover the similarities between the trace variants with incomplete traces and to predict the missing activity labels based on the succession relation matrix [23]. Furthermore, trace clustering is often a first step in applying more complex preprocessing techniques, such as statistical inferencebased analysis, aiming to reduce the complexity of an event log [24].…”

Section: A Review Of Event Log Preprocessing Techniquesmentioning

confidence: 99%

Event Log Data Quality Issues and Solutions

et al. 2023

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Process mining is a discipline that analyzes real event data extracted from information systems that support a business process to construct as-is process models and detect performance issues. Process event data are transformed into event logs, where the level of data quality directly impacts the reliability, validity, and usefulness of the derived process insights. The literature offers a taxonomy of preprocessing techniques and papers reporting on solutions for data quality issues in particular scenarios without exploring the relationship between the data quality issues and solutions. This research aims to discover how process mining researchers and practitioners solve certain data quality issues in practice and investigates the nature of the relationship between data quality issues and preprocessing techniques. Therefore, a study was undertaken among prominent process mining researchers and practitioners, gathering information regarding the perceived importance and frequency of data quality issues and solutions and the participants’ recommendations on preprocessing technique selection. The results reveal the most important and frequent data quality issues and preprocessing techniques and the gap between their perceived frequency and importance. Consequently, an overview of how researchers and practitioners solve data quality issues is presented, allowing the development of recommendations.

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“…Missing data in event logs has been defined as one of the major data quality issues in process mining [4,5]. Several methods were proposed in the field of process mining to repair event logs with Information 2022, 13, 234 2 of 18 missing data [6][7][8][9][10]. However, none of these methods can accurately repair missing activity labels in event logs when a large number of activity labels are missing.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach for Repairing Missing Activity Labels in Event Logs for Process Mining

Chen

Poon

2022

Information

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Process mining is a relatively new subject that builds a bridge between traditional process modeling and data mining. Process discovery is one of the most critical parts of process mining, which aims at discovering process models automatically from event logs. Like other data mining techniques, the performance of existing process discovery algorithms can be affected when there are missing activity labels in event logs. In this paper, we assume that the control-flow information in event logs could be useful in repairing missing activity labels. We propose an LSTM-based prediction model, which takes both the prefix and suffix sequences of the events with missing activity labels as input to predict missing activity labels. Additional attributes of event logs are also utilized to improve the performance. Our evaluation of several publicly available datasets shows that the proposed method performed consistently better than existing methods in terms of repairing missing activity labels in event logs.

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A repairing missing activities approach with succession relation for event logs

Cited by 6 publications

References 35 publications

Log Clustering-based Method for Repairing Missing Traces with Context Probability Information

Log Clustering-based Method for Repairing Missing Traces with Context Probability Information

Event Log Data Quality Issues and Solutions

A Deep Learning Approach for Repairing Missing Activity Labels in Event Logs for Process Mining

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