Likelihood-based Multiple Imputation by Event Chain Methodology for Repair of Imperfect Event Logs with Missing Data

Sim, Sunghyun; Bae, Hyerim; Choi, Yongki

doi:10.1109/icpm.2019.00013

Cited by 11 publications

(18 citation statements)

References 21 publications

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“…Quality of data can be improved by (i) improving the way in which data are captured while they are being generated and (ii) improving the data after they have been acquired [8]. The above studies [10,11] are (ii), while our study is (i). By using these two perspective methods together, it is expected that data quality can be further improved.…”

Section: Introductionmentioning

confidence: 92%

“…Various studies have been done on the quality of event logs. Many of these studies have proposed algorithms for repairing defects in the event log, such as the presence of missing values in activities and timestamps [3,10,11,16,[20][21][22][23][24][25][26][27][28][29][30][31]. By using these methods, we can obtain higher quality data that are close to the actual business process from the data with missing values.…”

Section: Related Workmentioning

confidence: 99%

“…In the area of process mining, research on pre-processing of event logs has been conducted. Sim et al proposed a likelihood-based Multiple Imputation to complete the missing values for events in the event log [10]. Conforti et al proposed a method for repairing the timestamp with which an event was executed in the event log [11].…”

Section: Introductionmentioning

confidence: 99%

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

Horita

Kurihashi

Miyamori

2020

Data

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In recent years, process mining has been attracting attention as an effective method for improving business operations by analyzing event logs that record what is done in business processes. The event log may contain missing data due to technical or human error, and if the data are missing, the analysis results will be inadequate. Traditional methods mainly use prediction completion when there are missing values, but accurate completion is not always possible. In this paper, we propose a method for understanding the tendency of missing values in the event log using decision tree learning without supplementing the missing values. We conducted experiments using data from the incident management system and confirmed the effectiveness of our method.

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Section: Introductionmentioning

confidence: 92%

Section: Related Workmentioning

confidence: 99%

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

Horita

Kurihashi

Miyamori

2020

Data

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“…Additionally, long short-term memory (LSTM) is an artificial neural network in deep learning, able to predict the missing event and activity labels in event logs [17]. Another technology enabling the resolution of missing data issues is likelihood-based algorithms, i.e., single imputation by event relationship (SIER) and multiple imputation by event chain (MIEC), which are able to repair event logs with missing events, timestamps, and resources [18]. 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].…”

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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“…Suriadi et al (2017) adapted a pattern-based approach to identify and repair event log quality issues. Sim et al (2019) proposed an event chain-based solution to repair missing activities as well as missing resources and attributes. Kong et al (2019) predicted missing events through identifying sound conditions in the log.…”

Section: Related Workmentioning

confidence: 99%

Prefix Imputation of Orphan Events in Event Stream Processing

Zaman

Hassani

2021

Front. Big Data

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In the context of process mining, event logs consist of process instances called cases. Conformance checking is a process mining task that inspects whether a log file is conformant with an existing process model. This inspection is additionally quantifying the conformance in an explainable manner. Online conformance checking processes streaming event logs by having precise insights into the running cases and timely mitigating non-conformance, if any. State-of-the-art online conformance checking approaches bound the memory by either delimiting storage of the events per case or limiting the number of cases to a specific window width. The former technique still requires unbounded memory as the number of cases to store is unlimited, while the latter technique forgets running, not yet concluded, cases to conform to the limited window width. Consequently, the processing system may later encounter events that represent some intermediate activity as per the process model and for which the relevant case has been forgotten, to be referred to as orphan events. The naïve approach to cope with an orphan event is to either neglect its relevant case for conformance checking or treat it as an altogether new case. However, this might result in misleading process insights, for instance, overestimated non-conformance. In order to bound memory yet effectively incorporate the orphan events into processing, we propose an imputation of missing-prefix approach for such orphan events. Our approach utilizes the existing process model for imputing the missing prefix. Furthermore, we leverage the case storage management to increase the accuracy of the prefix prediction. We propose a systematic forgetting mechanism that distinguishes and forgets the cases that can be reliably regenerated as prefix upon receipt of their future orphan event. We evaluate the efficacy of our proposed approach through multiple experiments with synthetic and three real event logs while simulating a streaming setting. Our approach achieves considerably higher realistic conformance statistics than the state of the art while requiring the same storage.

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Likelihood-based Multiple Imputation by Event Chain Methodology for Repair of Imperfect Event Logs with Missing Data

Cited by 11 publications

References 21 publications

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

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

Event Log Data Quality Issues and Solutions

Prefix Imputation of Orphan Events in Event Stream Processing

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