BIG DATA Laboratory

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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