Towards Event Log Querying for Data Quality

Andrews, Robert; Suriadi, Suriadi; Ouyang, Chun

doi:10.1007/978-3-030-02610-3_7

Cited by 18 publications

(12 citation statements)

References 23 publications

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“…There are also studies that have detected problems related to the quality of event logs [40,41] and proposed evaluation methods [42,43]. These are similar to our study, but our study is different in target because it specializes in tendency extraction of missing value.…”

Section: Related Worksupporting

confidence: 63%

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: Related Worksupporting

confidence: 63%

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

Horita

Kurihashi

Miyamori

2020

Data

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“…Secondly, research is performed on data quality assessment, targeting the systematic identification of event log quality issues. In this respect, the Rpackage DaQAPO [20], the log query language QUELI [1], and the CP-DQF [12] for Electronic Health Records (EHRs) provide tools and frameworks to operationalise data quality assessment. They are based on the event log quality issues defined in Vanbrabant et al [31], Suriadi et al [29], and Bose et al [5], respectively.…”

Section: Simulation For Capacity Management Decisions In Healthcare mentioning

confidence: 99%

The Need for Interactive Data-Driven Process Simulation in Healthcare: A Case Study

Hulzen

Martin

Depaire

2021

Lecture Notes in Business Information Processing

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In healthcare, more and more process execution information is stored in Hospital Information Systems. This data, in conjunction with data-driven process simulation, can be used, e.g. to support hospital management with Capacity Management decisions. However, real-life event logs in healthcare often suffer from data quality issues, affecting the reliability of simulation results. In this work, we illustrate the effects of disregarding data quality issues on simulation outcomes and the importance of domain knowledge using a case study at the radiology department of a hospital. Current literature on data-driven process simulation acknowledges the need for domain expertise but does not provide a framework for conceptualising the involvement of domain experts. Therefore, we propose a novel conceptual framework which interactively involves experts during data-driven simulation model development.

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“…Wang et al (2013) presented a pioneering work for treating noisy event data and proposed a backtracking algorithm for the recovery of a case. Andrews et al (2018) envisioned an event log query language that directly detects log imperfection issues. Suriadi et al (2017) adapted a pattern-based approach to identify and repair event log quality issues.…”

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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Towards Event Log Querying for Data Quality

Cited by 18 publications

References 23 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

The Need for Interactive Data-Driven Process Simulation in Healthcare: A Case Study

Prefix Imputation of Orphan Events in Event Stream Processing

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