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Within motorsports less experienced drivers lack pace and performance compared to their peers. Training these drivers requires time, which, due to the regulations and resources, teams often do not have. Less experienced drivers are expected to perform at the same level as experienced drivers. This paper has the aim of analyzing the abilities and performances of both drivers within a Formula One team to redesign the driver training method. The focus is to provide drivers with real-time insights and feedback on their performance during a simulator training session. By using a combination of the principles of process mining and statistical analysis, data markers are created on the track. Based on the differences in telemetry, visual feedback is provided to the driver. Throughout the research, this manner of training has proven to be promising. Drivers showed an increase in their overall performance and an increase in car control and confidence. Despite these promising results more exper iments need to be done to guarantee a consistent outcome and to prove the effectiveness of this training program. To continue developments, further research can be conducted on the topic of visualization and communication.

The FAIRification of data facilitates a fast-paced, global, FAIR metadata availability across domains for the sustainable growth of public/private organizations. Likewise, in healthcare, the clinical labs aim to achieve FAIR (biosample) metadata by keeping patient-specific infectious disease records. However, the responsible evaluation of the FAIRification process of Dutch clinical lab metadata is lacking at the local and global levels. From a responsible data science perspective, we normatively (in-principle) and empirically (in-practice) evaluate the Dutch clinical lab metadata share against FACT principles. The normative evaluation involved content analysis of FAIR concerning peer-reviewed publications and official websites. The empirical evaluation comprised a documentation review of standardized (public/confidential) documents regarding the metadata share of Dutch clinical labs. The evaluations assisted us in formulating two REA models based on REA ontology. The first REA model depicts the clinical lab metadata production run at a local/national level. The second REA model specifies the work (flow) breakdown structure of global FAIRification for FHIR Netherland using linkage relationship against FACT principles. In-field (IT and REA ontology) experts further evaluated the REA models for functional and structural veracity. Furthermore, our evaluations verified the presence of an underlying privacy-utility tradeoff in FAIRification of clinical lab metadata where data utility is prioritized over data protection.

Process mining derives knowledge of the execution of processes through analyzing behavior as observed from real-life events. While benefits of process mining are widely acknowledged, finding an adequate level of detail at which a mined process model is suitable for a specific stakeholder is still an ongoing challenge. Process models can be mined at different levels of abstraction, often resulting in either highly complex or highly abstract process models. This may have an important impact on the comprehensibility of the process model, which can also differ from the perspective of a particular stakeholder. To address this problem from a stakeholder-centric perspective, we propose a methodology for determining an appropriate level of process model abstraction. To this end, we use quantitative metrics on process models as well as a qualitative evaluation by using a technology acceptance model (TAM). A logistics case study involving the fuzzy process mining discovery algorithm shows init ial evidence that the use of appropriate abstraction levels is key when considering the needs of various stakeholders.

A bottleneck usually is a sub-process in the main process which delays the process. The performance of a process can be increased by eliminating the bottlenecks. To this end, opportunities to analyze and mitigate bottlenecks by using process mining techniques can be an interesting direction to utilize. This paper aims to classify literature on process mining bottleneck analysis techniques and propose a model for operational support regarding bottleneck analysis utilizing process mining. To this end, we first propose a model for classifying bottleneck analysis techniques. Then, we conduct a systematic literature review to identify existing papers that address bottleneck analysis by utilizing process mining techniques. The results indicate that many researchers are focusing on detecting bottlenecks, while limited attention is paid to predicting bottlenecks or recommending actions on what to do with bottlenecks. The proposed classification model is validated through a demonstration, showing how process mining bottleneck analysis techniques can be applied to a logistics case study.

EU through General Data Protection Regulation, GDPR, stipulates to safeguard EU citizens fundamental rights by ensuring ethical, uninterrupted, big data sharing within and outside EU. Healthcare data is no exception to this. While dealing with big data, healthcare providers, Big Data Analysts(BDAs) and government bodies have collectively realized that patients values are to be prioritized for patients optimal value care and for an efficient healthcare system at large. To ensure patients value care, privacy, inter alia, is incorporated both by design within each domain’s data base and by policy via international, pan-European and national laws and regulations. This also became viable by standardizing the Information Security Management System (ISMS) indicators for healthcare providers and regulators alike. Lack of standard respective metrics for each privacy assuring parameter, constrains privacy from becoming an objective value object for each value actor. Still, privacy can be seen transforming from being a subjective value for each value actor to a (subjective) value object in healthcare setup. To confirm this concept, this paper is based on two value models. Both models are built using E3- value kit on the guidelines of Padlock Chain Model in a Dutch healthcare setting. First model is built to represent the current state of privacy protecting data/information sharing between patients and healthcare providers (from the patients perspective). Later, the focus is drawn upon the privacy assuring bilateral relationships between Lab (biobank and bio-depositary) and other key value actors. In future our endeavor will be to quantitatively measure privacy by design constituents i.e. Minimization, Enforcement and Transparency at the backdrop of privacy by policy (ISMS) indicators i.e. availability, integrity, confidentiality and accountability. Experts opinions are included to evaluate the viability of the model discussed for privacy-ensuring healthcare data sharing both on healthcare sector and on technical grounds.

EU through General Data Protection Regulation, GDPR, stipulates to safeguard EU citizens fundamental rights by ensuring ethical, uninterrupted, big data sharing within and outside EU. Healthcare data is no exception to this. While dealing with big data, healthcare providers, Big Data Analysts(BDAs) and government bodies have collectively realized that patients values are to be prioritized for patients optimal value care and for an efficient healthcare system at large. To ensure patients value care, privacy, inter alia, is incorporated both by design within each domain’s data base and by policy via international, pan-European and national laws and regulations. This also became viable by standardizing the Information Security Management System (ISMS) indicators for healthcare providers and regulators alike. Lack of standard respective metrics for each privacy assuring parameter, constrains privacy from becoming an objective value object for each value actor. Still, privacy can be seen transforming from being a subjective value for each value actor to a (subjective) value object in healthcare setup. To confirm this concept, this paper is based on two value models. Both models are built using E3- value kit on the guidelines of Padlock Chain Model in a Dutch healthcare setting. First model is built to represent the current state of privacy protecting data/information sharing between patients and healthcare providers (from the patients perspective). Later, the focus is drawn upon the privacy assuring bilateral relationships between Lab (biobank and bio-depositary) and other key value actors. In future our endeavor will be to quantitatively measure privacy by design constituents i.e. Minimization, Enforcement and Transparency at the backdrop of privacy by policy (ISMS) indicators i.e. availability, integrity, confidentiality and accountability. Experts opinions are included to evaluate the viability of the model discussed for privacy-ensuring healthcare data sharing both on healthcare sector and on technical grounds.

Healthcare data has long been handled as per the national\local legislative and organizational prerequisites of the healthcare providers across the EU. This inculcates that the healthcare providers goals/objectives substantially dictated the data handling paths of the patients. Where the former could be starkly different from that of the patients/data subjects. Recently, healthcare stakeholders, for patients optimal value care, embedded the idea of Informed Consent (IC) at the onset of data collection. To transform the former, subjective way of data handling by healthcare providers into an objective pan European data handling procedure, the EU introduced General Data Protection Regulation, GDPR. Post GDPR, the EU legitimized patients IC as an integral part at the onset of patients information value care. Now IC, entails patients explicit, well informed , specific, free choice for his/her healthcare data collection and processing, demonstrated in an easily accessible and understandable manner with patients having the right of erasure at any point of data processing (GDPR, article 7). This poses limitations, specifically against scientific research, when applied in biobanks and bio-repositories. Limitations range from Biobanks data collection to data anonymization or/and pseudonymization to the data storage and sharing. In each phase of data handling, the issues and their respective solutions are discussed. Solution lies in defining the public/vital and legitimate interests and in incorporating the broad-open ended informed consent. This is vital for better health outcomes of the patients and the society at large. Regulatory and legislative authorities at the EU and national level must unite to resolve these issues.

This report concerns a white paper on the demonstration of practically-oriented guidelines for process mining users. The main target group of this document is novice process mining users (e.g., those who are willing to start using process mining). After proposing the guideline, we present a few exercise questions.

Non-availability incidents in public telecom services may have a wide-spread impact, such as disruption of internet services, mobile services, and land-line communication. This, in turn, may disrupt the life of consumers and citizens, and the provision of services by commercial and public organizations. These incidents are always analyzed and solved by the provider. In Europe, there is a legal obligation to report the analysis and solution of the incident to the national telecom regulator. However, these reports are highly confidential, and beyond some elementary descriptive statistics, they are not analyzed. This means that a significant opportunity is missed to draw lessons from these incidents, which could be valuable to other providers and to standardization bodies. In the LINC project, we aim to develop a method to draw lessons learned from registered non-availability incidents without compromising the confidentiality of those registrations. As a preparation for that, we have conducted a systematic literature review of non-availability incidents in public telecom services reported in the scientific and professional literature, to see what we can learn from the reported incident model and analysis methods used. In this report, we present an incident analysis taxonomy to establish a common terminological ground among researchers and practitioners.

Continuous delivery and rapidly changing requirements in agile environments force the developers to put non-functional requirements (NFRs) on halt till maintenance phase. However, neglecting NFRs during prioritization phase may lead to inaccurate estimations for software projects resulting in high maintenance cost and failures. The subjective and uncertain nature of non-functional requirements makes them unfit to be prioritized using conventional prioritization methods. Although the existing literature reports on inadequate consideration given to NFRs prioritization, still no comprehensive systematic effort has been done to report the limitations and evaluation mechanisms of existing NFRs prioritization approaches. Requirements engineering society lacks a broad understanding of NFRs prioritization approaches and the challenges which need to be overcome. Therefore, we aim to investigate (i) the existing NFR prioritization techniques and their validation mechanisms, (ii) the role of Artificial Intelligence (AI) in NFRs prioritization, and (iii) the limitations of existing NFRs prioritization techniques. For this, we reviewed the literature published from 2008 till present and extracted 30 studies. The results reveal twenty-five NFRs prioritization techniques out of which only three are AI based. The major limitations we have come across are that most of the NFRs prioritization techniques are not scalable to large datasets, inter-dependencies between functional requirements (FRs) and NFRs are ignored, and the uncertainties associated with NFRs are not considered at all. However, the literature suggests that AI-based techniques and Fuzzy logic may be used to solve issues such as uncertainties i.e. ambiguities, vagueness, and subjective opinions of stakeholders. This review adds to the existing body of knowledge on NFRs and motivates the practitioners to focus on the NFR prioritization by highlighting the limitations of the existing methods.

After a large incident on a telecommunications network, the operator typically executes an incident analysis to prevent future incidents. Research suggests that these analyses are done ad hoc, without a structured approach. In this paper, we conduct an investigation of a large incident according to the AcciMap method. We find that this method can be applied to telecommunications networks with a few small changes; we find that such a structured approach yields many more actionable recommendations than a more focused approach and we find that both the onset of an incident and the resolution phase merit their own analysis. We also find that such an analysis costs a lot of effort and we propose a more efficient approach to using this method. An unexpected outcome was that AcciMap may also be very useful for analyzing crisis organizations.

After a risk has manifested itself and has led to an accident, valuable lessons can be learned that can be taken into account to reduce the risk of a similar accident occurring again. This calls for accident analysis methods. In the past 20 years a large number of accident analysis methods have been proposed and it is difficult to find the right method to apply in a specific circumstance. We conducted a review of the state of the art of accident analysis methods and models across domains. We classify the models using the well-known categorization into sequential, epidemiological and systemic methods. We find that these classes have their own characteristics in terms of speed of application versus pay-off. For optimum risk reduction, methods that take organizational issues into account can add valuable information to the risk management process in an organization.