Abstract.Design Science Research is about the design of artefacts and their study in context. Design Science is widely used in the field of Information Systems Research and, in the past decade, also in Empirical Software Engineering. In a research project, Design Science is used to first identify the type of problem at hand and then follow the right steps to solve it. Data Analytics projects have a different focus and foundation, compared to information systems delivery projects or software development projects. In Data Analytics all the discussions revolve around the data, preparation, and interpretation. Whereas, in software or systems engineering projects, the focus is on the product. In this tutorial, we will explore the possible ways of applying design science research methodologies to Data Analytics research projects. To this end, we will first examine the existing data science methodologies from design science perspective. Second, existing design science methodologies such as Wierienga’s design science research methodology and Peffer’s design science methodology, will be examined from a data science perspective. Data science-focused factors such as data preparation, analytics, interpretation, privacy and baises will be discussed as part of research-methodological steps.

Abstract.Decisions are everywhere. Modelling decisions is important in processes, information systems, service applications, analytics, and so many other areas. So modelling decisions in the correct way is vital. This tutorial is about decision modelling, the new decision model and notation (DMN) standard, and the role of decisions as an integral part of information systems engineering.

Abstract.The volume, variety and velocity of products in software-intensive systems product lines is increasing. Product comparison is difficult when each product has hundreds of features. Reasons for product comparison include (i) concern for sustainability reasons whether to build a new product or not (ii) evaluating how products differ for strategic positioning reasons (iii) gauging if a product line needs to be reorganized (iv) assessing if a product falls within legislative and regulatory boundaries. We will describe a product comparison approach using similarity matching. A product configured from a product line feature model is represented as a weighted binary string. The similarity between products is compared using a binary string metric. The allocation of feature weights is contested. We will describe one weight allocation method based on a feature’s position in the feature model. We will discuss the benefits and limitations of this method using a mobile phone example.

Abstract.This tutorial delves into the transformative power of asking effective questions in engineering information systems. We explore how crafting well-defined questions in both the problem space (what issue are we addressing?) and the solution space (how will we approach it?) is paramount for success. The session will unveil the intricate relationship between these questions – how the “what” shapes the “how” and vice versa. We move beyond the fear of asking “naive” questions, demonstrating how these can spark innovation and reveal hidden assumptions. By the end, attendees will have a powerful and easy-to-use technique that removes the fear from questions.