Towards a better assessment of event logs quality

Kherbouche, Mohammed Oussama; Laga, Nassim; Masse, Pierre-Aymeric

doi:10.1109/ssci.2016.7849946

Cited by 12 publications

(7 citation statements)

References 13 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: 59%

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: 59%

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

Horita

Kurihashi

Miyamori

2020

Data

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“…some timestamps were recorded at day-level, whereas others at millisecond-level granularity), null values, and the level of uniqueness of attributes. Kherbouche et al [52] implemented an event log quality assessment framework in ProM 1 which considers complexity (e.g. number of events or average trace length), accuracy (e.g.…”

Section: Data Quality Issues In Event Logsmentioning

confidence: 99%

Supporting capacity management decisions in healthcare using data-driven process simulation

Hulzen

Martin

Depaire

et al. 2022

Journal of Biomedical Informatics

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“…Next review of challenging tasks was represented in Process Mining Manifesto [12] published in 2011. In contradiction to the research agenda [11] the challenges declared in the mani- [17 -19]; -maturity levels for event logs [12]; -a quantitative model to measure quality of event logs [27] event logs preparation for data streams mining (obtaining, cleaning, integration, selection, transformation, and definition of quality metrics) 2…”

Section: Challenging Tasks Of Process Miningmentioning

confidence: 99%

Process Mining: Applied Discipline and Software Implementations

Batyuk¹,

Voityshyn²

2018

NV KPI

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Background. A precise picture of how business processes (in the interpretation by Andrea Burattin) are performed in real-life is vitally important for an organization because it shows actual situation revealing gaps and bottlenecks. Process mining is a discipline with the purpose to research processes using as the input so-called event data (or event logs) which in essence is a digital footprint left in IT systems as the result of business processes execution. Objective. The goal of the study is to overview current state of process mining and find actual scientific and practice tasks in this field as well as justify and formalize requirements to the information technologies with the purpose to implement the found set of process mining applied tasks. Methods. The method used by the authors to prepare current overview consisted of the following steps: (a) analysis of information sources; (b) finding and formalization of actual scientific as well as practice tasks; (c) description of the requirements to the information technologies with the purpose to implement the found set of actual tasks. Results. It has been found out that process mining as an applied discipline has been actively developed for 20 years; significant contribution to creating the scientific basis of process mining has been done in Eindhoven University of Technology (The Netherlands) under direction of professor Wil M.P. van der Aalst. It also has been found actual scientific and practice tasks of process mining: event data preparation, dealing with concept drift, operational support, event data streams processing, handling big event data, improving process mining tools usability for the end users. It has been formalized requirements and specified quality attributes for the information technologies with the purpose to implement the found actual tasks. Architecture of the information technologies has been proposed by the authors. Conclusions. Currently the theoretical core of process mining has mainly been developed and quite structured. However, despite of the fact that mathematical methods and software tools have been successfully used in practice for a few years, the request for the intellectual business process analysis has not been fulfilled yet. The authors have found out that relevant information technologies should supply such functions as handling big event logs, dealing with event data streams as well as operational support of business processes which are at the execution stage.

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Towards a better assessment of event logs quality

Cited by 12 publications

References 13 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

Supporting capacity management decisions in healthcare using data-driven process simulation

Process Mining: Applied Discipline and Software Implementations

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