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This study explores how process managers perceive the adoption, use and management of process mining in practice. While research in process mining predominantly focuses on the technical aspects, our work highlights organizational and managerial implications.

We report on a focus group study conducted with process managers from various industries in Central Europe. This setting allowed us to gain diverse and in-depth insights about the needs and expectations of practitioners in relation to the adoption, use and management of process mining.

We find that process managers face four central challenges. These challenges are largely related to four stages; (1) planning and business case calculation, (2) process selection, (3) implementation, and (4) process mining use.

We point to research opportunities in relation to the adoption, use and management of process mining. We suggest that future research should apply interdisciplinary study designs to better understand the managerial and organizational implications of process mining.

The reported challenges have various practical implications at the organizational and managerial level. We explore how existing BPM frameworks can be extended to meet these challenges.

This study is among the first attempts to explore process mining from the perspective of process managers. It clarifies important challenges and points to avenues for future research.

The purpose of this paper is to present a comparison of a number of business process mining tools currently available in the UK market. An outline of the practice of business process mining is given, along with an analysis of the main techniques developed by academia and commercial entities. This paper also acts as a primer for the acceptance and further use of process mining in industry, suggesting future directions for this practice.

Secondary research has been completed to establish the main commercial business process mining tool vendors for the market. A literature survey has also been undertaken into the latest theoretical techniques being developed in the field of business process mining.

The authors have identified a number of existing commercially available business process mining tools and have listed their capabilities within a comparative analysis table. All commercially available business process mining tools included in this paper are capable of process comparison and at least 40 per cent of the tools claim to deal with noise in process data.

The contribution of this paper is to provide a state‐of‐the‐art review of a number of commercial business process mining tools available within the UK. This paper also presents a summary of the latest research being undertaken in academia in this subject area and future directions for the practice of business process mining.

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.

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.

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.

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.

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.

Process mining is a research area used to discover, monitor and improve real business processes by extracting knowledge from event logs available in process-aware information systems. The purpose of this paper is to evaluate the application of artificial neural networks (ANNs) and support vector machines (SVMs) in data mining tasks in the process mining context. The goal was to understand how these computational intelligence techniques are currently being applied in process mining.

The authors conducted a systematic literature review with three research questions formulated to evaluate the use of ANNs and SVMs in process mining.

The authors identified 11 papers as primary studies according to the criteria established in the review protocol. Most of them deal with process mining enhancement, mainly using ANNs. Regarding the data mining task, the authors identified three types of tasks used: categorical prediction (or classification); numeric prediction, considering the “regression” type, and clustering analysis.

Although there is scientific interest in process mining, little attention has been specifically given to ANNs and SVM. This scenario does not reflect the general context of data mining, where these two techniques are widely used. This low use may be possibly due to a relative lack of knowledge about their potential for this type of problem, which the authors seek to reverse with the completion of this study.

This paper seeks to examine the area of business process mining, providing an overview of state‐of‐the‐art techniques. An outline of the main problems experienced in the practice of process mining is given along with reference to work that addresses the most challenging issues experienced in this field. This paper also aims to examine the application of soft computing techniques to process‐mining problems.

This paper is based on a comprehensive review of literature covering more than 50 research papers. These papers are analysed to identify current trends and future research directions in the field.

Process‐mining techniques are now becoming available as graphical interface‐driven software tools, where flow diagram representations of processes may be manipulated as part of the mining task. A significant number of papers employ mining heuristics to aid in the task of process discovery. Soft computing algorithms are increasingly being investigated to aid the accuracy and speed of mining algorithms. Many papers exist that address common mining problems such as noise and mining loops. However, problems such as duplicate tasks, mining perspectives and delta analysis require further research.

The contribution of this paper is to provide a summary of the current trends in process‐mining practice and point out future research directions. A review of the work in this new and expanding area has been provided in the form of illustrative graphs and tables that identify the trends in this area. This is the most comprehensive and up‐to‐date review of business process‐mining literature.

Learning management systems (LMS) provide detailed information about the processes through event-logs. Process and related data-mining approaches can reveal valuable information from these files to help teachers and executives to monitor and manage their online learning processes. In this regard, the purpose of this paper is to present an overview of the current direction of the literature on educational data mining, and an application framework to analyze the educational data provided by the Moodle LMS.

The paper presents a framework to provide a decision support through the approaches existing in process and data-mining fields for analyzing the event-log data gathered from LMS platforms. In this framework, latent class analysis (LCA) and sequential pattern mining approaches were used to understand the general patterns; heuristic and fuzzy approaches were performed for process mining to obtain the workflows and statistics; finally, social-network analysis was conducted to discover the collaborations.

The analyses conducted in the study give clues for the process performance of the course during a semester by indicating exceptional situations, clarifying the activity flows, understanding the main process flow and revealing the students’ interactions. Findings also show that using the preliminary data analyses before process mining steps is also beneficial to understand the general pattern and expose the irregular ones.

The study highlights the benefits of analyzing event-log files of LMSs to improve the quality of online educational processes through a case study based on Moodle event-logs. The application framework covers preliminary analyses such as LCA before the use of process mining algorithms to reveal the exceptional situations.

This study aims to analyse the deep resource mining that causes high in situ stress, and the disturbance of tunnelling and mining which may induce large stress concentration, plastic deformation and rock strata compression deformation. The depth of deep resources, excavation rate and multilayered heterogeneity are critical factors of excavation disturbance in deep rock. However, at present, there are few engineering practices used in deep resource mining, and it is difficult to analyse the high in situ stress and dynamic three-dimensional (3D) excavation process in laboratory experiments. As a result, an understanding of the behaviours and mechanisms of the dynamic evolution of the stress field and plastic zone in deep tunnelling and mining surrounding rock is still lacking.

This study introduced a 3D engineering-scale finite element model and analysed the scheme involved the elastoplastic constitutive and element deletion techniques, while considering the influence of the deep rock mass of the roadway excavation, coal seam mining-induced stress, plastic zone in the process of mining disturbance of the in situ stress state, excavation rate and layered rock mass properties at the depths of 500 m, 1,500 m and 2,500 m of several typical coal seams, and the tunnelling and excavation rates of 0.5 m/step, 1 m/step and 2 m/step. An engineering-scale numerical model of the layered rock and soil body in an actual mining area were also established.

The simulation results of the surrounding rock stress field, dynamic evolution and maximum value change of the plastic zone, large deformation and settlement of the layered rock mass are obtained. The numerical results indicate that the process of mining can be accelerated with the increase in the tunnelling and excavation rate, but the vertical concentrated stress induced by the surrounding rock intensifies with the increase in the excavation rate, which becomes a crucial factor affecting the instability of the surrounding rock. The deep rock mass is in the high in situ stress state, and the stress and plastic strain maxima of the surrounding rock induced by the tunnelling and mining processes increase sharply with the excavation depth. In ultra-deep conditions (depth of 2,500 m), the maximum vertical stress is quickly reached by the conventional tunnelling and mining process. Compared with the deep homogeneous rock mass model, the multilayered heterogeneous rock mass produces higher mining-induced stress and plastic strain in each layer during the entire process of tunnelling and mining, and each layer presents a squeeze and dislocation deformation.

The results of this study can provide a valuable reference for the dynamic evolution of stress and plastic deformation in roadway tunnelling and coal seam mining to investigate the mechanisms of in situ stress at typical depths, excavation rates, stress concentrations, plastic deformations and compression behaviours of multilayered heterogeneity.

Process mining provides a generic collection of techniques to turn event data into valuable insights, improvement ideas, predictions, and recommendations. This paper uses spreadsheets as a metaphor to introduce process mining as an essential tool for data scientists and business analysts. The purpose of this paper is to illustrate that process mining can do with events what spreadsheets can do with numbers.

The paper discusses the main concepts in both spreadsheets and process mining. Using a concrete data set as a running example, the different types of process mining are explained. Where spreadsheets work with numbers, process mining starts from event data with the aim to analyze processes.

Differences and commonalities between spreadsheets and process mining are described. Unlike process mining tools like ProM, spreadsheets programs cannot be used to discover processes, check compliance, analyze bottlenecks, animate event data, and provide operational process support. Pointers to existing process mining tools and their functionality are given.

Event logs and operational processes can be found everywhere and process mining techniques are not limited to specific application domains. Comparable to spreadsheet software widely used in finance, production, sales, education, and sports, process mining software can be used in a broad range of organizations.

The paper provides an original view on process mining by relating it to the spreadsheets. The value of spreadsheet-like technology tailored toward the analysis of behavior rather than numbers is illustrated by the over 20 commercial process mining tools available today and the growing adoption in a variety of application domains.

client/server(C/S) systems have revolutionized the systems development approach. Among the drivers of the C/S systems is the lower price/performance ratio compared to the mainframe‐based transaction processing systems. Data mining is a process of identifying patterns in corporate transactional and operational databases (also called data warehouses). As most Fortune 500 companies are moving quickly towards the client server systems, it is increasingly becoming important that a data mining approaches should be adapted for C/S systems. In the current paper, we describe different data mining approaches that are used in the C/S systems.

The purpose of this study is to examine whether the compliance management activities in the risk management environment of financial institutions can be enhanced using a Process Mining application.

In this research, an implementation procedure for a selected Process Mining application is developed and evaluated at a financial institution in Germany.

The evaluation of the process data with the Process Mining application Disco shows that the compliance of the real-life execution of business processes can be monitored in real-time. Moreover, potential non-compliant activities and durations can be analysed in a detailed manner.

When the research results are regarded, it must be considered that a general condition for the usage of a Process Mining application is that the process data is available and exportable in the required format and that data privacy regulations are fulfilled.

This research presents a practical use case for the implementation of a Process Mining application at the risk management department of financial institutions. It shows the value of using a technical application to carry out tedious tasks that are usually executed manually. This value is discussed and compared with the aim to help financial institutions in determining how the effectiveness and efficiencies of compliance management activities can be improved. Therefore, this research can be taken as a foundation for the practical implementation of a Process Mining application at financial institutions.