Event Log Preprocessing for Process Mining: A Review

Process Mining allows organizations to obtain actual business process models from event logs (discovery), to compare the event log or the resulting process model in the discovery task with the existing reference model of the same process (conformance), and to detect issues in the executed process to...

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Autores principales:	Heidy M. Marin-Castro, Edgar Tello-Leal
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	process mining data preprocessing data quality event log noise event data diversity Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/d7a00b8fc9fb4e969ef8eb8896429195
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spelling	oai:doaj.org-article:d7a00b8fc9fb4e969ef8eb88964291952021-11-25T16:31:26ZEvent Log Preprocessing for Process Mining: A Review10.3390/app1122105562076-3417https://doaj.org/article/d7a00b8fc9fb4e969ef8eb88964291952021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10556https://doaj.org/toc/2076-3417Process Mining allows organizations to obtain actual business process models from event logs (discovery), to compare the event log or the resulting process model in the discovery task with the existing reference model of the same process (conformance), and to detect issues in the executed process to improve (enhancement). An essential element in the three tasks of process mining (discovery, conformance, and enhancement) is data cleaning, used to reduce the complexity inherent to real-world event data, to be easily interpreted, manipulated, and processed in process mining tasks. Thus, new techniques and algorithms for event data preprocessing have been of interest in the research community in business process. In this paper, we conduct a systematic literature review and provide, for the first time, a survey of relevant approaches of event data preprocessing for business process mining tasks. The aim of this work is to construct a categorization of techniques or methods related to event data preprocessing and to identify relevant challenges around these techniques. We present a quantitative and qualitative analysis of the most popular techniques for event log preprocessing. We also study and present findings about how a preprocessing technique can improve a process mining task. We also discuss the emerging future challenges in the domain of data preprocessing, in the context of process mining. The results of this study reveal that the preprocessing techniques in process mining have demonstrated a high impact on the performance of the process mining tasks. The data cleaning requirements are dependent on the characteristics of the event logs (voluminous, a high variability in the set of traces size, changes in the duration of the activities. In this scenario, most of the surveyed works use more than a single preprocessing technique to improve the quality of the event log. Trace-clustering and trace/event level filtering resulted in being the most commonly used preprocessing techniques due to easy of implementation, and they adequately manage noise and incompleteness in the event logs.Heidy M. Marin-CastroEdgar Tello-LealMDPI AGarticleprocess miningdata preprocessingdata qualityevent lognoise eventdata diversityTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10556, p 10556 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	process mining data preprocessing data quality event log noise event data diversity Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	process mining data preprocessing data quality event log noise event data diversity Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Heidy M. Marin-Castro Edgar Tello-Leal Event Log Preprocessing for Process Mining: A Review
description	Process Mining allows organizations to obtain actual business process models from event logs (discovery), to compare the event log or the resulting process model in the discovery task with the existing reference model of the same process (conformance), and to detect issues in the executed process to improve (enhancement). An essential element in the three tasks of process mining (discovery, conformance, and enhancement) is data cleaning, used to reduce the complexity inherent to real-world event data, to be easily interpreted, manipulated, and processed in process mining tasks. Thus, new techniques and algorithms for event data preprocessing have been of interest in the research community in business process. In this paper, we conduct a systematic literature review and provide, for the first time, a survey of relevant approaches of event data preprocessing for business process mining tasks. The aim of this work is to construct a categorization of techniques or methods related to event data preprocessing and to identify relevant challenges around these techniques. We present a quantitative and qualitative analysis of the most popular techniques for event log preprocessing. We also study and present findings about how a preprocessing technique can improve a process mining task. We also discuss the emerging future challenges in the domain of data preprocessing, in the context of process mining. The results of this study reveal that the preprocessing techniques in process mining have demonstrated a high impact on the performance of the process mining tasks. The data cleaning requirements are dependent on the characteristics of the event logs (voluminous, a high variability in the set of traces size, changes in the duration of the activities. In this scenario, most of the surveyed works use more than a single preprocessing technique to improve the quality of the event log. Trace-clustering and trace/event level filtering resulted in being the most commonly used preprocessing techniques due to easy of implementation, and they adequately manage noise and incompleteness in the event logs.
format	article
author	Heidy M. Marin-Castro Edgar Tello-Leal
author_facet	Heidy M. Marin-Castro Edgar Tello-Leal
author_sort	Heidy M. Marin-Castro
title	Event Log Preprocessing for Process Mining: A Review
title_short	Event Log Preprocessing for Process Mining: A Review
title_full	Event Log Preprocessing for Process Mining: A Review
title_fullStr	Event Log Preprocessing for Process Mining: A Review
title_full_unstemmed	Event Log Preprocessing for Process Mining: A Review
title_sort	event log preprocessing for process mining: a review
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/d7a00b8fc9fb4e969ef8eb8896429195
work_keys_str_mv	AT heidymmarincastro eventlogpreprocessingforprocessminingareview AT edgartelloleal eventlogpreprocessingforprocessminingareview
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Event Log Preprocessing for Process Mining: A Review

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