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; data semantics to improve information retrieval and automate data integration; declarative languages (algebra, calculus) to manipulate data and workflows; and highly distributed and parallel environments such as P2P, cluster and cloud. To reflect our approach, we organize our research program in five complementary themes:

  1. Data integration, including polystores;
  2. Query processing in the cloud, including indexing and privacy;
  3. Scientific workflows, in the context of cluster and cloud;
  4.  Data analytics, including data mining and statistics;
  5. Machine learning for high-dimensional data processing and search.


  • 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 search, content-based image retrieval

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