Our objective is to support reasoning tasks in heritage architecture with graphics enabling analysts to visualise and share their understanding of how, from a given set of information, alternative scenarios or evolution can be inferred. The paper comments on the nature of the cognitive processes in historical sciences, and on factors that need to be weighed when interpreting sets of information. Visual solutions are proposed, and illustrated on real cases in Kraków Poland. They help spotting where alternative explanations should be considered in order to avoid unjustified assumptions and certitudes on the evolution of artefacts. The contribution expects to demonstrate that reasoning on uncertainties in historical sciences can be fruitfully backed up by concepts and practices from the infovis community.
Tag Archives: Uncertainty
Learning Skills from Data Based on XML Structured Qualification Profiles
In this paper we address and discuss the approach of learning employee skills from data based on XML structured profiles and their representation as a Bayesian network. For extracting new information we use a dependency analysis approach. Many enterprise resource management systems (ERP) come along with integrated modules for Human Resource Management (HRM). One main task of HRM is to manage, improve and deploy the right skills at the right time. These processes are well known as skill management. Furthermore the problem of finding hidden or implicit dependencies between employee skills is considered. Using an information theoretical approach to construct a powerful skill representation as graphical model is recommendable. To demonstrate the achievement of the learned network structure, a test scenario is given, where historical reference project data is used.