The new distance measure is an improvement over the current standard whenever a hierarchical arrangement of categorical values is available.

Abstract. Biomedical research requires deep domain expertise to perform analyses of complex data sets, assisted by mathematical expertise provided by data scientists who design and develop sophisticated methods and tools. Such methods and tools not only require preprocessing of the data, but most of all a meaningful input selection. Usually, data scientists do not have sufficient background knowledge about the origin of the data and the biomedical problems to be solved, consequently a doctor-in-the-loop can be of great help here. In this paper we revise the viability of integrating an analysis guided visualization component in an ontology-guided data infrastructure, exemplified by the principal component analysis. We evaluated this approach by examining the potential for intelligent support of medical experts on the case of cerebral aneurysms research.

Processing and exploring large quantities of electronic data is often a particularly interesting but yet challenging task. Both the lack of statistical and mathematical skills and the missing know-how of handling masses of (health) data constitute high barriers for profound data exploration -especially when performed by domain experts. This paper presents guided visual pattern discovery, by taking the wellestablished data mining method Principal Component Analysis as an example. Without guidance, the user has to be conscious about the reliability of computed results at any point during the analysis (GIGOprinciple). In the course of the integration of principal component analysis into an ontology-guided research infrastructure, we include a guidance system supporting the user through the separate analysis steps and we introduce a quality measure, which is essential for profound research results.