Welcome to the Knowledge Discovery & Management Group

The DII research group on Knowledge Discovery and Management focuses on methodologies and systems for business intelligence (data warehouse, data mining), business innovation, semantic enterprise interoperability, collaborative distributed systems. The group has gained years of experience in data mining field, ranging from pre-processing to model induction methods. In particular, the group has developed and continuously improves the Bayes Vector Quantizer (BVQ) algorithm, a multi-class cost-sensitive learning technique for the design of optimal classifiers and the definition of optimal sets of features in risk-based, class-unbalanced domains. The group develops semantic models for the semantic enrichment of structured objects, like Data Mining models, processes, and Key Performance Indicators. This research is aimed at supporting interoperability of managerial activities in distributed collaborative environments. At present these interdisciplinary competencies are exploited in the design and development of two support systems, the KDDVM platform for the collaborative design and execution of knowledge discovery and e-science experiments, and the BIVEE platform for Open Innovation in business ecosystems. Visit the Project and Publications pages for more information on current and past projects and results. Students who like to work on these topics can also visit the Theses page.