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The purpose of this paper is to explore the feasibility that an ontological approach can be applied to formalize the construction regulation constraint knowledge in a computer-interpretable way, for construction quality checking, during construction stage.

The ontological and semantic web technologies are used to model the construction quality constraints knowledge into Axioms/OWL and SWRL rules. Protégé platform is selected to illustrate how the construction quality checking, based on the Axioms/OWL and SWRL rules, is achieved.

The ontology and semantic web technologies can be an alternative way for modeling the construction regulation constraints in a computer-interpretable way, and can be implemented for the regulation-based construction quality checking.

The approach is illustrated only with given specific technical constraints examples, the generality and practicality of the approach need further investigation.

The paper introduces an ontological and semantic approach to model and formalize the construction regulation constraints for construction quality checking, and proves the feasibility by the case studies. The proposed approach enables the regulations can be understood and retrieved semantically by computers, which facilitates the using of regulation codes.

The purpose of this paper is to specify the decomposition conditions of Wand and Weber for the Business Process Model and Notation (BPMN). Therefore, an interpretation of the conditions for BPMN is derived and compared to a specification of the conditions for enhanced Event-Driven Process Chains (eEPCs). Based on these results, guidelines for a conformance check of BPMN and eEPC models with the decomposition conditions are shown. Further, guidelines for decomposition are formulated for BPMN models. The usability of the decomposition guidelines is tested with modelling experts.

An approach building on a representational mapping is used for specifying the decomposition conditions. Therefore, ontological constructs of the Bunge-Wand-Weber ontology are mapped to corresponding modelling constructs and an interpretation of the decomposition conditions for BPMN is derived. Guidelines for a conformance check are then defined. Based on these results, decomposition guidelines are formulated. Their usability is tested in interviews.

The research shows that the decomposition conditions stemming from the information systems discipline can be transferred to business process modelling. However, the interpretation of the decomposition conditions depends on specific characteristics of a modelling language. Based on a thorough specification of the conditions, it is possible to derive guidelines for a conformance check of process models with the conditions. In addition, guidelines for decomposition are developed and tested. In the study, these are perceived as understandable and helpful by experts.

Research approaches based on representational mappings are subjected to subjectivity. However, by having three researchers performing the approach independently, subjectivity can be mitigated. Further, only ten experts participated in the usability test, which is therefore to be considered as a first step in a more comprising evaluation.

This paper provides the process modeller with guidelines enabling a conformance check of BPMN and eEPC process models with the decomposition conditions. Further, guidelines for decomposing BPMN models are introduced.

This paper is the first to specify Wand and Weber's decomposition conditions for process modelling with BPMN. A comparison to eEPCs shows, that the ontological expressiveness influences the interpretation of the conditions. Further, guidelines for decomposing BPMN models as well as for checking their adherence to the decomposition conditions are presented.

Organizations rely on their business processes to achieve their business objectives and ensure compliance with relevant laws and regulations. Hence, conformance to process specifications is essential to remain compliant. Various factors influence an organization’s ability to operate in conformance to its process specifications. This study investigates the influence of business process management (BPM)-supportive culture and individual process orientation on process conformance.

A construct was created for perceived process conformance and two constructs were selected from literature to represent BPM-supportive culture and individual process orientation. A survey was conducted with 178 employees of a global enterprise, hypotheses were formulated, and a statistical model was constructed and validated.

Results pinpoint the key role of the BPM-supportive culture in influencing both individual process orientation and conformance. Individual process orientation is also found to have a significant influence on process conformance. The findings provide additional evidence for the significance of human-related aspects of BPM in achieving BPM success.

The contributions of this paper help better understand how soft factors of BPM contribute to employees’ process conformance drawing on and relating concepts of BPM and organizational routines.

Quality assurance (QA) with regard to structures, systems and components is also an important task during the design and operational phase of a repository for the disposal of radioactive waste. The first step is to evaluate the technical design on the basis of a comprehensive safety assessment. The results of this evaluation can then be used in order to classify structures, systems and components into different QA areas. Type and volume of the necessary documentation depend on the relevance of the structures, systems and components to safety. Describes the application of this general procedure including design changes and maintenance aspects during operation, for the planned Konrad waste repository in Germany.

This paper is a response to a consultative document by the Personal Investment Authority (PIA), the UK financial services industry regulator. The PIA exists, not because the industry is a monopoly or concerned with health or safety, but because of the unsatisfactory quality it has provided to its customers. The approach of the paper is to examine if the quality management principles developed in manufacturing during past years could be useful in addressing this problem. The paper appears in two parts; the second part proposes a different type of regulatory system.

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.

Process mining (PM) has emerged as a leading technology for gaining data-based insights into organizations’ business processes. As processes increasingly cross-organizational boundaries, firms need to conduct PM jointly with multiple organizations to optimize their operations. However, current knowledge on cross-organizational process mining (coPM) is widely dispersed. Therefore, we synthesize current knowledge on coPM, identify challenges and enablers of coPM, and build a socio-technical framework and agenda for future research.

We conducted a literature review of 66 articles and summarized the findings according to the framework for Information Technology (IT)-enabled inter-organizational coordination (IOC) and the refined PM framework. The former states that within inter-organizational relationships, uncertainty sources determine information processing needs and coordination mechanisms determine information processing capabilities, while the fit between needs and capabilities determines the relationships’ performance. The latter distinguishes three categories of PM activities: cartography, auditing and navigation.

Past literature focused on coPM techniques, for example, algorithms for ensuring privacy and PM for cartography. Future research should focus on socio-technical aspects and follow four steps: First, determine uncertainty sources within coPM. Second, design, develop and evaluate coordination mechanisms. Third, investigate how the mechanisms assist with handling uncertainty. Fourth, analyze the impact on coPM performance. In addition, we present 18 challenges (e.g. integrating distributed data) and 9 enablers (e.g. aligning different strategies) for coPM application.

This is the first article to systematically investigate the status quo of coPM research and lay out a socio-technical research agenda building upon the well-established framework for IT-enabled IOC.

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 evaluate educational data mining methods to increase the predictive accuracy of student academic performance for a university course setting. Student engagement data collected in real time and over self-paced activities assisted this investigation.

Classification data mining techniques have been adapted to predict students’ academic performance. Four algorithms, Naïve Bayes, Logistic Regression, k-Nearest Neighbour and Random Forest, were used to generate predictive models. Process mining features have also been integrated to determine their effectiveness in improving the accuracy of predictions.

The results show that when general features derived from student activities are combined with process mining features, there is some improvement in the accuracy of the predictions. Of the four algorithms, the study finds Random Forest to be more accurate than the other three algorithms in a statistically significant way. The validation of the best-known classifier model is then tested by predicting students’ final-year academic performance for the subsequent year.

The present study was limited to datasets gathered over one semester and for one course. The outcomes would be more promising if the dataset comprised more courses. Moreover, the addition of demographic information could have provided further representations of students’ performance. Future work will address some of these limitations.

The model developed from this research can provide value to institutions in making process- and data-driven predictions on students’ academic performances.

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.