Privacy as human flourishing: Could a shift towards virtue ethics strengthen privacy protection in the age of Big Data?

Privacy is commonly seen as an instrumental value in relation to negative freedom, human dignity and personal autonomy. Article 8 ECHR, protecting the right to privacy, was originally coined as a doctrine protecting the negative freedom of citizens in vertical relations, that is between citizen and state. Over the years, the Court has extended privacy protection to horizontal relations and has gradually accepted that individual autonomy is an equally important value underlying the right to privacy. However, in most of the recent cases regarding Article 8 ECHR, the Court goes beyond the protection of negative freedom and individual autonomy and instead focuses self-expression, personal development and human flourishing. Accepting this virtue ethical notion, in addition to the traditional Kantian focus on individual autonomy and human dignity, as a core value of Article 8 ECHR may prove vital for the protection of privacy in the age of Big Data.

Ten Questions for Future Regulation of Big Data: A Comparative and Empirical Legal Study

Much has been written about Big Data from a technical, economical, juridical and ethical perspective. Still, very little empirical and comparative data is available on how Big Data is approached and regulated in Europe and beyond. This contribution makes a first effort to fill that gap by presenting the reactions to a survey on Big Data from the Data Protection Authorities of fourteen European countries and a comparative legal research of eleven countries. This contribution presents those results, addressing 10 challenges for the regulation of Big Data.

Privacy in the Post-NSA Era: Time for a Fundamental Revision?

Big Brother Watch and others have filed a complaint against the United Kingdom under the European Convention on Human Rights about a violation of Article 8, the right to privacy. It regards the NSA affair and UK-based surveillance activities operated by secret services. The question is whether it will be declared admissible and, if so, whether the European Court of Human Rights will find a violation. This article discusses three possible challenges for these types of complaints and analyses whether the current privacy paradigm is still adequate in view of the development known as Big Data.

Action Research and Learning Analytics in Higher Education

Teaching is a dynamic activity. It can be very effective, if its impact is constantly monitored and adjusted to the demands of changing social contexts and needs of learners. This implies that teachers need to be aware about teaching and learning processes. Moreover, they should constantly question their didactical methods and the learning resources, which they provide to their students. They should reflect if their actions are suitable, and they should regulate their teaching, e.g., by updating learning materials based on new knowledge about learners, or by motivating learners to engage in further learning activities. In the last years, a rising interest in ‘learning analytics’ is observable. This interest is motivated by the availability of massive amounts of educational data. Also, the continuously increasing processing power, and a strong motivation for discovering new information from these pools of educational data, is pushing further developments within the learning analytics research field. Learning analytics could be a method for reflective teaching practice that enables and guides teachers to investigate and evaluate their work in future learning scenarios. However, this potentially positive impact has not yet been sufficiently verified by learning analytics research. Another method that pursues these goals is ‘action research’. Learning analytics promises to initiate action research processes because it facilitates awareness, reflection and regulation of teaching activities analogous to action research. Therefore, this thesis joins both concepts, in order to improve the design of learning analytics tools. Central research question of this thesis are: What are the dimensions of learning analytics in relation to action research, which need to be considered when designing a learning analytics tool? How does a learning analytics dashboard impact the teachers of technology-enhanced university lectures regarding ‘awareness’, ‘reflection’ and ‘action’? Does it initiate action research? Which are central requirements for a learning analytics tool, which pursues such effects? This project followed design-based research principles, in order to answer these research questions. The main contributions are: a theoretical reference model that connects action research and learning analytics, the conceptualization and implementation of a learning analytics tool, a requirements catalogue for useful and usable learning analytics design based on evaluations, a tested procedure for impact analysis, and guidelines for the introduction of learning analytics into higher education.

Konzeption eines proaktiven Risikomanagements in Logistiknetzwerken

Logistiknetzwerke von Unternehmen wachsen sehr schnell und werden immer komplexer. Unternehmen wissen oft nicht, von welchen anderen Unternehmen sie abhängig sind und welche geschäftskritischen Risiken sich daraus für sie ergeben. Aus diesem Grund wird in diesem Artikel ein Konzept eines proaktiven Ri-sikomanagements in Logistiknetzwerken vorgestellt. Das Konzept basiert auf der Big Data Technologie und verwendet zur Identifikation von Risiken und zum Aufbau eines Logistiknetzwerkes neben internen Unternehmensdaten auch externe Daten, z. B. Social Media Plattformen oder andere Datenportale. Diese Daten werden ausgewertet und mit Risiken behaftete Beziehungen werden dem Bediener grafisch angezeigt. Zusätzlich dazu kann das System dem Benutzer mögliche Alternativen zur Vermeidung dieser Risiken aufzeigen und somit zur Entscheidungsunterstützung genutzt werden.

Development of a novel material flow simulation model for the integration of spatial and process relevant information

Simulation techniques are almost indispensable in the analysis of complex systems. Materials- and related information flow processes in logistics often possess such complexity. Further problem arise as the processes change over time and pose a Big Data problem as well. To cope with these issues adaptive simulations are more and more frequently used. This paper presents a few relevant advanced simulation models and intro-duces a novel model structure, which unifies modelling of geometrical relations and time processes. This way the process structure and their geometric relations can be handled in a well understandable and transparent way. Capabilities and applicability of the model is also presented via a demonstrational example.