Situating requirements engineering methods within design science research

Design Science Research Methodologies (DSRM) are increasingly used to guide research in fields beyond Information Systems, in particular those of Requirements Engineering and Software Engineering (RE/SE). While a number of DSR methodologies have been developed by scholars in the RE/SE fields, there remains a certain level of confusion about the way in which the aim and scope of DSRM and those of methods typically used in RE/SE differ. This issue can be observed in graduate students' work as well as in published literature. In particular, the difference be-tween the research orientation of DSRM and the solution orientation of RE/SE methods can be difficult to navigate. We propose to address this challenge by situating three RE/SE methodologies proposed in published literature within one common DSRM; doing so clarifies the scope of these methodologies and highlights ways in which the knowledge contributions of their results could be further enhanced. This effort is a first step towards providing better guidance to researchers who are new to design science research in order to ensure that recognized DSR principles are promoted and respected

Methods and Tools for Management of Distributed Event Processing Applications

Die Erfassung und Verarbeitung von Ereignissen aus cyber-physischen Systemen bietet Anwendern die Möglichkeit, kontinuierlich über Leistungsdaten und aufkommende Probleme unterrichtet zu werden (Situational Awareness) oder Wartungsprozesse zustandsabhängig zu optimieren (Condition-based Maintenance). Derartige Szenarien verlangen aufgrund der Vielzahl und Frequenz der Daten sowie der Anforderung einer echtzeitnahen Auswertung den Einsatz geeigneter Technologien. Unter dem Namen Event Processing haben sich dabei Technologien etabliert, die in der Lage sind, Datenströme in Echtzeit zu verarbeiten und komplexe Ereignismuster auf Basis räumlicher, zeitlicher oder kausaler Zusammenhänge zu erkennen. Gleichzeitig sind heute in diesem Bereich verfügbare Systeme jedoch noch durch eine hohe technische Komplexität der zugrunde liegenden deklarativen Sprachen gekennzeichnet, die bei der Entwicklung echtzeitfähiger Anwendungen zu langsamen Entwicklungszyklen aufgrund notwendiger technischer Expertise führt. Gerade diese Anwendungen weisen allerdings häufig eine hohe Dynamik in Bezug auf Veränderungen von Anforderungen der zu erkennenden Situationen, aber auch der zugrunde liegenden Sensordaten hinsichtlich ihrer Syntax und Semantik auf. Der primäre Beitrag dieser Arbeit ermöglicht Fachanwendern durch die Abstraktion von technischen Details, selbständig verteilte echtzeitfähige Anwendungen in Form von sogenannten Echtzeit-Verarbeitungspipelines zu erstellen, zu bearbeiten und auszuführen. Die Beiträge der Arbeit lassen sich wie folgt zusammenfassen: 1. Eine Methodik zur Entwicklung echtzeitfähiger Anwendungen unter Berücksichtigung von Erweiterbarkeit sowie der Zugänglichkeit für Fachanwender. 2. Modelle zur semantischen Beschreibung der Charakteristika von Ereignisproduzenten, Ereignisverarbeitungseinheiten und Ereigniskonsumenten. 3. Ein System zur Ausführung von Verarbeitungspipelines bestehend aus geographisch verteilten Ereignisverarbeitungseinheiten. 4. Ein Software-Artefakt zur graphischen Modellierung von Verarbeitungspipelines sowie deren automatisierter Ausführung. Die Beiträge werden in verschiedenen Szenarien aus den Bereichen Produktion und Logistik vorgestellt, angewendet und evaluiert

Breakthroughs and emerging insights from ongoing design science projects: Research-in-progress papers and poster presentations from the 11th international conference on design science research in information systems and technology (DESRIST) 2016. St. John, Newfoundland, Canada, May 23-25

This volume contains selected research-in-progress papers and poster presentations from DESRIST 2016 - the 11th International Conference on Design Science Research in Information Systems and Technology held during 24-25 May 2016 at St. John's, Newfoundland, Canada. DESRIST provides a platform for researchers and practitioners to present and discuss Design Science research. The 11th DESRIST built on the foundation of ten prior highly successful international conferences held in Claremont, Pasadena, Atlanta, Philadelphia, St. Gallen, Milwaukee, Las Vegas, Helsinki, Miami, and Dublin. This year's conference places a special emphasis on using Design Science to engage with the growing challenges that face society, including (but not limited to) demands on health care systems, climate change, and security. With these challenges in mind, individuals from academia and industry came together to discuss important ongoing work and to share emerging knowledge and ideas. Design Science projects often involve multiple sub-problems, meaning there may be a delay before the final set of findings can be laid out. Hence, this volume "Breakthroughs and Observations from Ongoing Design Science Projects" presents preliminary findings from studies that are still underway. Completed research from DESRIST 2016 is presented in a separate volume entitled "Tackling Society's Grand Challenges with Design Science", which is published by Springer International Publishing, Switzerland. The final set of accepted papers in this volume reflects those presented at DESRIST 2016, including 11 research-in-progress papers and 4 abstracts for poster presentations. Each research-in-progress paper and each poster abstract was reviewed by a minimum of two referees. We would like to thank the authors who submitted their research-in-progress papers and poster presentations to DESRIST 2016, the referees who took the time to construct detailed and constructive reviews, and the Program Committee who made the event possible. Furthermore we thank the sponsoring organisations, in particular Maynooth University, Claremont Graduate University, and Memorial University of Newfoundland, for their financial support. We believe the research described in this volume addresses some of the most topical and interesting design challenges facing the field of information systems. We hope that readers find the insights provided by authors as valuable and thought-provoking as we have, and that the discussion of such early findings can help to maximise their impact

A data management and analytic model for business intelligence applications

Most organisations use several data management and business intelligence solutions which are on-premise and, or cloud-based to manage and analyse their constantly growing business data. Challenges faced by organisations nowadays include, but are not limited to growth limitations, big data, inadequate analytics, computing, and data storage capabilities. Although these organisations are able to generate reports and dashboards for decision-making in most cases, effective use of their business data and an appropriate business intelligence solution could achieve and retain informed decision-making and allow competitive reaction to the dynamic external environment. A data management and analytic model has been proposed on which organisations could rely for decisive guidance when planning to procure and implement a unified business intelligence solution. To achieve a sound model, literature was reviewed by extensively studying business intelligence in general, and exploring and developing various deployment models and architectures consisting of naïve, on-premise, and cloud-based which revealed their benefits and challenges. The outcome of the literature review was the development of a hybrid business intelligence model and the accompanying architecture as the main contribution to the study.In order to assess the state of business intelligence utilisation, and to validate and improve the proposed architecture, two case studies targeting users and experts were conducted using quantitative and qualitative approaches. The case studies found and established that a decision to procure and implement a successful business intelligence solution is based on a number of crucial elements, such as, applications, devices, tools, business intelligence services, data management and infrastructure. The findings further recognised that the proposed hybrid architecture is the solution for managing complex organisations with serious data challenges.ComputingM. Sc. (Computing