Extraction of Missing Tendency Using Decision Tree Learning in Business Process Event Log

Horita, Hiroki; Kurihashi, Yuta; Miyamori, Nozomi

doi:10.3390/data5030082

Cited by 5 publications

(3 citation statements)

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“…When such dependencies do not exist or the event log contains limited attribute data, effective repair of the event log may not be possible. In [18], a decision tree learning algorithm is proposed to discover rules for missing values in event logs. In [19], a novel classification event imputation method is proposed, which can recover missing categorical events by learning structural features observed in the event log.…”

Section: A Attribute-level Repairmentioning

confidence: 99%

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%

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

Fang,

2024

ijacsa

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“…Furthermore, the random forest algorithm is a machine learning classification algorithm able to detect events with an inaccurate event timestamp in the event logs [19]. Another classification algorithm applied in process mining is the classification and regression tree (CART) algorithm applied to discover the tendency of missing data in an event log without repairing the data [20]. Natural language processing (NLP) is a subfield of machine learning closely related to artificial intelligence, enabling machines to understand human language.…”

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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“…Instead of trying to repair missing data in event logs, Horita et al [31] applied the decision tree learning algorithm to discover the tendency of missing values in event logs. The output of [31] is a decision tree that indicates the conditions that there is likely to have missing data in event logs (e.g., there is an event with a missing activity label when a certain activity happens before it).…”

Section: Missing Data In Event Logsmentioning

confidence: 99%

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

Chen

Poon

2022

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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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Extraction of Missing Tendency Using Decision Tree Learning in Business Process Event Log

Cited by 5 publications

References 44 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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