About our Research

Our current research focuses on a wide range of topics in AI and Data Science, and it is organised in two research areas.

  • KRDB: research area for Knowledge and Data (director: Oliver Kutz)
    • Data preparation
      • semantic interoperability and data integration;
      • data preparation for analytics based on semantic metadata;
      • database restructuring to enable intelligent business processes;
      • intelligent information system deployment and maintenance.
    • Conceptual modelling
      • conceptual and ontology modelling for system design;
      • intelligent information systems that combine data, information, knowledge, and processes.
    • Semantic data access
      • semantic-based intelligent access to data;
      • natural language interfaces to knowledge.
    • Model construction
      • Ontology learning;
      • Process model extraction from text.
    • Process modelling and mining
      • business process modelling and process mining;
      • predictive and proactive process monitoring;
      • enhanced analysis techniques that combine simulation, operational research and prediction.
    • Predictive methods for multi-dimensional temporal data:
      • explainability techniques for multi-dimensional temporal data;
      • predictive methods with backgroud knolwedge.
    • Reasoning techniques and tools
      • formal and cognitive foundations of knowledge representation and reasoning; 
      • data and web standards.
  • D2AI: Data-driven Artificial Intelligence research area (director: Antonio Liotta)
    • Analysis and modelling of spatio-temporal datasets and databases
      • multi-dimensional data series in space and time;
      • complex scenarios such as smart systems, industrial IoT, and predictive maintenance;
    • Foundations and applications of advanced machine learning
      • methods and techniques for the creation of models based on both big and small data;
      • multi-task learning, transfer learning, and reinforcement learning;
      • machine learning in embedded systems.
    • Extraction of information from images and video
      • from low-level perception to high level interpretation;
      • activity recognition in video;
      • anomaly detection in images and volumetric data.
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