Spor

As companies gather more and more data, we need to find a way to allow interested decision makers to access this data in an efficient way. In the context of sports practice, users could benefit from suggestions about new sports they could try out and the company could increase its sales. This work aims to support the analysts, simultaneously domain experts and IT laymen, in their data exploration and suggestion retrieval tasks through a user friendly interface, abstracting away the complexity of formulating expressive queries into the visual domain. We present a characterization and task analysis for this domain, and a prototype that meets the requirements emerging from them, based on an interdisciplinary literature research. The resulting prototype combines a visual query language with a collaborative filtering approach to render suggestions for new activities, and show multiple types of relationships in a visually compelling way. It has been implemented as a web application that handles the transformation of user input from a graphical pattern into a database query language and the results of this query into an easy to digest information representation. We conclude with an expert interview to validate the design for analysis and exploration.