Cini Lab on Data Science

The research addressed in the CINI "Big Data" Laboratory ranges from methodological to technological and applicative topics.

In particular, the main METHODOLOGICAL subjects of interest are:

  • Data Modeling: Big Data models and Mega-modeling; Pervasive data management; High dimensionality reduction;  Large scale mobile & sensor data management; Hybrid data infrastructure; Data posting;
  • Information Extraction: Structuring Big Data (heterogenous, unstructured, structured Big Data); Representing/annotating multimedia Big Data; Entity linking; Topic detection; Entity identification;
  • Information integration: Semantic matching; On-the-fly data integration; Ontology-based access to Big Data.
  • Querying and retrieval: Algorithms for Big Data search; Semantic Technologies for Big Data querying and retrieval; Stream reasoning; Distributed and peer-to-peer search; Query languages for Big Data; Big Data for profiling.
  • Mining and analytics: Machine learning based on Big Data; Data Mining based on large scale heterogenous data; Streaming data analytics; Big Data metrics; Probabilistic models for Big Data; Computational intelligence models for Big Data; Visual analytics for Big Data; Real-time anomaly detection; Information network analysis.
  • Big Open Data: Linked open data publishing.
  • Algorithms for data intensive scalable computing: Algorithms and programming techniques for Big Data processing; Algorithms for large scale highly dynamic networks; Algorithms for external memory, MapReduce and datastream models; Algorithms for storage and indexing of massive data; Web algorithmics; Large scale graph analysis.
  • Privacy: Privacy preserving in Big Data;

The main TECHNOLOGICAL subjects of interest are:

  • Distributed computing and architectures: HPC techniques and cloud infrastructures for Big Data; performance engineering for big data computing;"systems of systems" architectural solutions;
  • Toolkits for Big Data Sources: Web-related data (content & logs), Social networks data (Facebook, Twitter, RenRen).
  • Open data portals.

 The main APPLICATIVE subjects of interest are:

  • Scientific applications of Big Data: Environmental monitoring; Climate change; Bioinformatics and system biology.
  • Social applications of Big Data: Large-scale social media analysis; Large-scale recommendation systems; Innovative services for Smart Cities, Smart Energy and Smart Transportation based on digital traces of human activities both in cyber environments (the Internet) and in the physical world.
  • Big Data for Enterprise and Government: Open government; Business model innovation; Enterprise transformation; Business process modeling; Software process modeling and engineering.
  • Big Data for security: Big Data for threat detection; Big Data in Cybersecurity.


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