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

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

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.

scholarly journals Process Mining in Supply Chains: A Systematic Literature Review

2018 ◽  
Vol 8 (6) ◽  
pp. 4626
Author(s):  
Bambang Jokonowo ◽  
Jan Claes ◽  
Riyanarto Sarno ◽  
Siti Rochimah
Keyword(s):  
Literature Review ◽  
Supply Chains ◽  
Process Improvement ◽  
Systematic Literature Review ◽  
Process Mining ◽  
Continuous Process ◽  
Process Models ◽  
Data Preparation ◽  
Continuous Process Improvement ◽  
Definition Of

Performance analysis and continuous process improvement efforts are often supported by the construction of process models representing the interactions of the partners in the supply chain. This study was conducted to determine the state of the art in the process mining field, specifically in the context of cross-organizational process. The Systematic Literature Review (SLR) method is used to review a collection of twenty-one papers that are classified according to the Artifact framework of Hevner, et al. and within the Process Mining framework of Van der Aalst. In the reviewed papers, the authors conducted a variety of techniques to establish the event log, which is then used to perform the process mining analysis. Eight of the reviewed papers focus on the definition of concepts or measures. Five of the papers describe models and other abstractions that are used as a theoretical basis for process mining in the context of supply chains. The majority twenty of papers describe some kind of informal method or formal algorithm to perform process mining analysis. Nine of the papers that propose a formal algorithm also present an accompanying software implementation. Eight papers discuss the data preparation challenges and twelve papers discuss process discovery techniques.

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