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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.

The need for assuring correctness of business processes in enterprises is widely recognised in terms of business process re-engineering and improvement. Formal methods are a promising approach to this issue. The challenge in business process verification is to create a formal model that is well-aligned to the reality. Process mining is a well-known technique to discover a model of a process based on facts. However, no studies exist that apply it to formal verification. This study aims to propose a methodology for formal business process verification by means of process mining, and attempts to clarify the challenges and necessary technologies in this approach using a case study.

A trading company simulation model is used as a case study. A workflow model is discovered from an event log produced by a simulation tool and manually complemented to a formal model. Correctness requirements of both domain-dependent and domain-independent types of the model are checked by means of model-checking.

For business process verification with both domain-dependent and domain-independent correctness requirements, more advanced process mining techniques that discover data-related aspects of processes are desirable. The choice of a formal modelling language is also crucial. It depends on the correctness requirements and the characteristics of the business process.

Formal verification of business processes starting with creating its formal model is quite new. Furthermore, domain-dependent and domain-independent correctness properties are considered in the same framework, which is also new. This study revealed necessary technologies for this approach with process mining.

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.

This paper aims to review the application of business process management (BPM) in the port sector. Its objective is to understand whether BPM principles are applied in the port sector, the role of the procedural factor in port performance evaluation and whether electronic data interchange systems have been used for process management purposes.

The objective of this research is to conduct a critical review of existing academic literature in the domain of BPM and its application in the ports sector. This paper assessed more than a hundred recent publications, from key journals in the domains of port economics, BPM and information technology. The two principle platforms used are the online databases of the World Bank Group and the University of Antwerp.

Academic literature reviewed reveals a partial application of BPM in the port and maritime sector. BPM related research is conducted via the utilization of modeling algorithms or optimization and simulation tools. There exists evidence that electronic data interchange (EDI) data extracted from EDI platforms can be used to model inter-organizational business processes in several industries. Yet, to the best of the author’s knowledge, no research investigates Port Community System (PCS) or single window (SW) data utilization for BPM purposes, although PCS and SW benefits are well documented. Port performance is largely assessed based on the production theory, and limited number of studies use elements of procedural efficiency as variables for their analysis.

The holistic application of BPM has been researched in numerous industries but in the port sector. This paper constitutes the first section of an original research study to define key components, assumptions and constraints for developing a comprehensive BPM framework in the port sector.

Mashups have been studied extensively in the literature; nevertheless, the large body of work in this area focuses on service/data level integration and leaves UI level integration, hence UI mashups, almost unexplored. The latter generates digital environments in which participating sources exist as individual entities; member applications and data sources share the same graphical space particularly in the form of widgets. However, the true integration can only be realized through enabling widgets to be responsive to the events happening in each other. The authors call such an integration “widget orchestration” and the resulting application “mashup by orchestration”. This article aims to explore and address challenges regarding the realization of widget‐based UI mashups and UI level integration, prominently in terms of widget orchestration, and to assess their suitability for building web‐based personal environments.

The authors provide a holistic view on mashups and a theoretical grounding for widget‐based personal environments. The authors identify the following challenges: widget interoperability, end‐user data mobility as a basis for manual widget orchestration, user behavior mining – for extracting behavioral patterns – as a basis for automated widget orchestration, and infrastructure. The authors introduce functional widget interfaces for application interoperability, exploit semantic web technologies for data interoperability, and realize end‐user data mobility on top of this interoperability framework. The authors employ semantically enhanced workflow/process mining techniques, along with Petri nets as a formal ground, for user behavior mining. The authors outline a reference platform and architecture that is compliant with the authors' strategies, and extend W3C widget specification respectively – prominently with a communication channel – to foster standardization. The authors evaluate their solution approaches regarding interoperability and infrastructure through a qualitative comparison with respect to existing literature, and provide a computational evaluation of the behavior mining approach. The authors realize a prototype for a widget‐based personal learning environment for foreign language learning to demonstrate the feasibility of their solution strategies. The prototype is also used as a basis for the end‐user assessment of widget‐based personal environments and widget orchestration.

The evaluation results suggest that the interoperability framework, platform, and architecture have certain advantages over existing approaches, and the proposed behavior mining techniques are adequate for the extraction of behavioral patterns. User assessments show that widget‐based UI mashups with orchestration (i.e. mashups by orchestration) are promising for the creation of personal environments as well as for an enhanced user experience.

This article provides an extensive exploration of mashups by orchestration and their role in the creation of personal environments. Key challenges are described, along with novel solution strategies to meet them.

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.

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.

Academic research into entrepreneurship policy is particularly interesting due to the increasing relevance of the topic and since knowledge about the evolution of themes in this field is still rather limited. The purpose of this paper is to analyse the key concepts, topics, trends and shifts that have shaped the entrepreneurship policy research agenda during the period 1990–2016.

This paper uses text mining techniques, cluster analysis and complementary bibliographic data to examine the evolution of a corpus of 1,048 academic papers focused on entrepreneurship-related policies and published during the period 1990–2016 in ten relevant journals. In particular, the paper follows a standard text mining workflow: first, as text is unstructured, content requires a set of pre-processing tasks and then a stemming process. Then, the paper examines the most repeated concepts within the corpus, considering the whole period 1990–2016 and also in five-year terms. Finally, the paper conducts a k-means clustering to divide the collection of documents into coherent groups with similar content. The analyses in the paper also include geographical particularities considering three regional sub-corpora, distinguishing those articles authored in the European Union (EU), the USA and South and Eastern Asia, respectively.

Results of the analysis show that inclusion, employment and regulation-related papers have largely dominated the research in the field, evolving from an initial classical approach to the relationship between entrepreneurship and employment to a wider, multidisciplinary perspective, including the relevance of management, geographies and narrower topics such as agglomeration economics or internationalisation instead of the previous generic sectorial approaches. The text mining analysis also reveals how entrepreneurship policy research has gained increasing attention and has become both more open, with a growing cooperation among researchers from different affiliations, and more sophisticated, with concepts and themes that moved the research agenda forward, closer to the priorities of policy implementation.

The paper identifies main trends and research gaps in the field of entrepreneurship policy providing actionable knowledge by presenting the spectrum of both over-explored and understudied research themes in the field. In practical terms the results of the text mining analysis can be interpreted as a compass to navigate the entrepreneurship policy research agenda.

The paper presents the heterogeneity of topics under research in the field, reinforcing the concept of entrepreneurship as a multidisciplinary and dynamic domain. Therefore, the definition and adoption of a certain policy agenda in entrepreneurship should consider multiple aspects (needs, objectives, stakeholders, expected outputs, etc.) to be comprehensive and aligned with its complexity. In addition, the paper shows how text mining techniques could be used to map the research activity in a particular field, contributing to the challenge of linking research and policy.

The exploratory nature of text mining allows us to obtain new knowledge and reveals hidden patterns from large quantities of documents/text data, representing an opportunity to complement other qualitative reviews. In this sense, the main value of this paper is not to advise on the future configuration of entrepreneurship policy as a research topic, but to unwrap the past by unveiling how key themes of the entrepreneurship policy research agenda have emerged, evolved and/or declined over time as a foundation on which to build further developments.