A semi-automatic methodology for the design of performance monitoring systems Claudia Diamantini, Laura Genga, Domenico Potena, Emanuele Storti Research Paper Dipartimento di Ingegneria dell’Informazione Universitá Politecnica delle Marche via Brecce Bianche, 60131 Ancona, Italy {c.diamantini,l.genga,d.potena,e.storti}@univpm.it Abstract. In the present work, we propose a methodology for the de- sign of a strategic support information system, aimed both at monitoring enterprise daily activities and at supporting decision making by means of Key Performance Indicators (KPIs). In particular, given a set of re- quested KPIs and the schemas of available data sources, our approach aims at identifying the subset of requested KPIs that can be actually computed over the sources. The KPIs are represented by means of an ontology, over which proper reasoning functionalities have been imple- mented. Both such automatic functionalities and interactions with ex- perts are required in order to map ontology concepts to schema elements. Keywords: Performance monitoring system design; Key Performance Indicators; Formula reasoning 1 Introduction During last years, performance monitoring has gained an increasing importance in enterprise management, due to its role in leading enterprises to achieve strate- gic goals in a cost-effective way [8]. Identifying (and proper evaluating) suitable key performance indicators (KPIs) with respect to enterprise goals plays a central role both in managing daily activities and in monitoring the degree of achieve- ment of long-term strategies. However, selection and monitoring of the right set of KPIs often turns out to be a non-trivial task, depending on goals to achieve and on expertise of managers. An intensive research effort has being performed in order to define methodologies and best practices to deal with such a topic, as shown by the huge amount of contributions in Literature devoted at the design of performance measuring systems (see e.g. [9] for an overall survey). Within such a context, a well-known issue regards how to match KPI definitions with the enterprise data sources; two main alternative approaches are usually exploited to this end, differing for the relative importance assumed by the ideal KPIs and by the real-world data, namely the “goal-driven” and the “data-driven” approach. In the former, the most relevant decisions are taken by the manager, whose focus is typically on the selection of KPIs to monitor, while little or no regard