Our approach is to capitalize on the principles of distributed and parallel data management. In particular, we exploit: high-level languages as the basis for data independence and automatic optimization; declarative languages to manipulate data and workflows; and highly distributed and parallel environments such as cluster and cloud for scalability and performance. We also exploit machine learning, probabilities and statistics for high-dimensional data processing, data analytics and data search.

  1. Distributed data management, including data integration and scientific workflows
  2. Big data management and parallel data management
  3.  Data analytics, including data mining and statistics
  4. Machine learning for high-dimensional data processing


  • Data science, big data, scientific data
  • Cluster, cloud, peer to peer
  • Distributed and parallel data management, data integration, data privacy,  data analytics, machine learning, data searching, content-based image retrieval

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