The DUKe (Data User Knowledge) research group, part of the LS2N laboratory (UMR CNRS 6004), University of Nantes, aims at proposing querying, mining and learning techniques that take into account

• data types (relational, spatial, graphical, temporal, stream, etc.),
• expert knowledge or user interactions through adapted visual supports

Moreover, the objective of the research team is to provide algorithms that 

• consider user-related data, in particular issues like privacy, fairness and the value of personal data
• show good properties in terms of user interaction : anytime, incremental, fast, user-knowledge accounting or graphical representation
• allow to observe, analyze users' usage and then propose a user-system coevolution

Part of our work has, over the years, established connections with other research fields that handle large data sets, which analysis requires introduction of field-specific expert knowledge into models (biology, ethology, history, sociology, literature and education sciences).

Our current application fields are :

• Enterprise of the future
  • Business Intelligence : customer relationship management, ...
  • Manufacturing intelligence : predictive maintenance, ...
  • Digital transformation : extraction and formalization of business knowledge
• Health of the future
  • Personalized medicine : genome-wide association studies, ...
  • Hospital of the future : treatment and immersion in virtual reality, ...
• Digital Humanities
  • Learning Analytics : Mooc log mining, recommendation of teaching resources, ...
  • Cultural heritage : helping historical resources annotation, recommendation of museographic resources ...
  • Sociology, epistemology : cross-mining of digital traces, surveys and interviews

Our goal is to propose User-centered methods for Data management, Mining or Machine Learning. This involves four challenges

• User involved in the process of data collection
• User involved in the learning/mining process
• Mining users activity data
• Learning / mining with relational, structured or linked data