scholarly journals A Survey on Concept Drift in Process Mining

2022 ◽  
Vol 54 (9) ◽  
pp. 1-38
Author(s):  
Denise Maria Vecino Sato ◽  
Sheila Cristiana De Freitas ◽  
Jean Paul Barddal ◽  
Edson Emilio Scalabrin
Keyword(s):  
Steady State ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Process Mining ◽  
Concept Drift ◽  
Evaluation Protocol ◽  
Offline Analysis

Concept drift in process mining (PM) is a challenge as classical methods assume processes are in a steady-state, i.e., events share the same process version. We conducted a systematic literature review on the intersection of these areas, and thus, we review concept drift in PM and bring forward a taxonomy of existing techniques for drift detection and online PM for evolving environments. Existing works depict that (i) PM still primarily focuses on offline analysis, and (ii) the assessment of concept drift techniques in processes is cumbersome due to the lack of common evaluation protocol, datasets, and metrics.

Process Mining Perspectives in Software Engineering: A Systematic Literature Review

2021 ◽  
Author(s):  
Silvia Jaqueline Urrea-Contreras ◽  
Brenda L. Flores-Rios ◽  
Maria Angelica Astorga-Vargas ◽  
Jorge E. Ibarra-Esquer
Keyword(s):  
Software Engineering ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Process Mining

How is process mining technology used by organizations? A systematic literature review of empirical studies

2018 ◽  
Vol 24 (4) ◽  
pp. 900-922 ◽  
Author(s):  
Malte Thiede ◽  
Daniel Fuerstenau ◽  
Ana Paula Bezerra Barquet
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Enterprise Resource Planning ◽  
Process Mining ◽  
Empirical Studies ◽  
Resource Planning ◽  
Classification Model ◽  
Organizational Process ◽  
Content Type ◽  
Cross System

Purpose The purpose of this paper is to review empirical studies on process mining in order to understand its use by organizations. The paper further aims to outline future research opportunities. Design/methodology/approach The authors propose a classification model that combines core conceptual elements of process mining with prior models from technology classification from the enterprise resource planning and business intelligence field. The model incorporates an organizational usage, a system-orientation and service nature, adding a focus on physical services. The application is based on a systematic literature review of 144 research papers. Findings The results show that, thus far, the literature has been chiefly concerned with realization of single business process management systems in single organizations. The authors conclude that cross-system or cross-organizational process mining is underrepresented in the ISR, as is the analysis of physical services. Practical implications Process mining researchers have paid little attention to utilizing complex use cases and mining mixed physical-digital services. Practitioners should work closely with academics to overcome these knowledge gaps. Only then will process mining be on the cusp of becoming a technology that allows new insights into customer processes by supplying business operations with valuable and detailed information. Originality/value Despite the scientific interest in process mining, particularly scant attention has been given by researchers to investigating its use in relatively complex scenarios, e.g., cross-system and cross-organizational process mining. Furthermore, coverage on the use of process mining from a service perspective is limited, which fails to reflect the marketing and business context of most contemporary organizations, wherein the importance of such scenarios is widely acknowledged. The small number of studies encountered may be due to a lack of knowledge about the potential of such scenarios as well as successful examples, a situation the authors seek to remedy with this study.

scholarly journals Process mining usage in cybersecurity and software reliability analysis : A systematic literature review

Array ◽  
2021 ◽  
pp. 100120
Author(s):  
Martin Macak ◽  
Lukas Daubner ◽  
Mohammadreza Fani Sani ◽  
Barbora Buhnova
Keyword(s):  
Literature Review ◽  
Reliability Analysis ◽  
Systematic Literature Review ◽  
Software Reliability ◽  
Process Mining

Process mining and industrial applications: A systematic literature review

2020 ◽  
Vol 27 (3) ◽  
pp. 225-233 ◽  
Author(s):  
Angelo Corallo ◽  
Mariangela Lazoi ◽  
Fabrizio Striani
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Process Mining ◽  
Industrial Applications

scholarly journals A systematic literature review on state-of-the-art deep learning methods for process prediction

2021 ◽  
Author(s):  
Dominic A. Neu ◽  
Johannes Lahann ◽  
Peter Fettke
Keyword(s):  
Deep Learning ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Process Mining ◽  
Learning Algorithm ◽  
Control Flow ◽  
Performance Measurements ◽  
Advantages And Disadvantages ◽  
Sequence Of Events ◽  
Process Prediction

AbstractProcess mining enables the reconstruction and evaluation of business processes based on digital traces in IT systems. An increasingly important technique in this context is process prediction. Given a sequence of events of an ongoing trace, process prediction allows forecasting upcoming events or performance measurements. In recent years, multiple process prediction approaches have been proposed, applying different data processing schemes and prediction algorithms. This study focuses on deep learning algorithms since they seem to outperform their machine learning alternatives consistently. Whilst having a common learning algorithm, they use different data preprocessing techniques, implement a variety of network topologies and focus on various goals such as outcome prediction, time prediction or control-flow prediction. Additionally, the set of log-data, evaluation metrics and baselines used by the authors diverge, making the results hard to compare. This paper attempts to synthesise the advantages and disadvantages of the procedural decisions in these approaches by conducting a systematic literature review.

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