Research Projects dissemination related to SEBD 2020

HOPE: High quality Open data Publishing and Enrichment

Reference: Prof. Maurizio Lenzerini - Sapienza University of Rome, Italy

The HOPE project has received funding from the Italian Research Ministry through the PRIN 2017 call (project code: 2017MMJJRE)

While open data have a huge potential in the data-driven society, many barriers of different types exist in publishing and using open data. HOPE aims at overcoming the main technical problems that current open data solutions suffer from, by developing a methodology and associated tools for a new way of producing, publishing, maintaining, accessing and exploiting privacy-preserving open data. The envisioned result of the project is a complete web-based semantic open data manager that an organization can use for governing the whole lifecycle of its open data, and the final users can access for effectively consuming the information provided by the organization. The project puts together 5 partners including the strongest leaders of the Italian Knowledge Representation and Data Management communities. If successful, this effort will have a deep impact on society, as it will help unchaining all the potentiality of open data for citizens and policy makers. The practical value of our approach will be evaluated by real users. Three Italian PA institutions are already collaborating with the partners of the consortium, and have agreed to experiment the tools resulting from the research results, providing feedbacks from the very beginning of the project.

Duration: 8/2019-8/2022

Project Website


ExaMode: Extreme-scale Analytics via Multimodal Ontology Discovery & Enhancement

Reference: Dr. Gianmaria Silvello - University of Padua, Italy

The ExaMode project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 825292.

ExaMode: Driven by data, developed for patients. Exascale volumes of diverse data from distributed sources are continuously produced. Healthcare data stand out in the size produced (production is expected to be over 2000 exabytes in 2020), heterogeneity (many media, acquisition methods), included knowledge (e.g. diagnosis) and commercial value. The supervised nature of deep learning models requires large labeled, annotated data, which precludes models to extract knowledge and value. Examode solves this by allowing easy and fast, weakly supervised knowledge discovery of exascale heterogeneous data, limiting human interaction.

The main objectives:

  1. Weakly-supervised knowledge discovery for exascale medical data;
  2. Develop extreme scale analytic tools for heterogeneous exascale multimodal and multimedia data;
  3. Healthcare and industry decision-making adoption of extreme-scale analysis and prediction tools.

Duration: 1/1/2019-31/12/2022

Project Website - Project Flyer


MASTER: Multiple ASpects TrajEctoRy management and analysis

Reference: Dr. Chiara Renso - HPC Lab, ISTI-CNR

Funded by Horizon 2020 call H2020-MSCA-RISE-2017 and GA n.777695

MASTER (Multiple ASpects TrajEctoRy management and analysis)  is a project coordinated by ISTI-CNR and funded under the call H2020-MSCA-RISE-2017. The project started on March 1 st , 2018 and lasts 48 months. The objective of MASTER is forming an international and inter-sectoral network of organisations working on a joint research programme to define new methods to build, manage and analyse multiple aspects semantic trajectories.

The Consortium counts ten partners, including academic and non-academic institutions. The European academic partners are the National Research Council (Italy), the University of Venice Ca’ Foscari (Italy), the University of Piraeus Research Center (Greece), the University of Versailles Saint Quentin (France) and the Harokopio University (Greece). The European non-academic partner is Thira Municipality (Greece). The International academic partners are Federal University of Santa Catarina (Brazil), Federal University of Ceara’ (Brazil), Dalhousie University (Canada).

We convey the idea that pure movement data can be enriched with multiple heterogeneous contextual aspects. These aspects are intimately interconnected and should be referenced as a whole as holistic trajectories.

The scientific concept of MASTER is driven by research challenges around the definition, management and analysis of holistic trajectories and potential market opportunities benefitting the European society in the field of tourism, sea monitoring and public transportation. We propose methods to analyze and infer knowledge from holistic trajectories, considering as vital issues the privacy and Big Data dimensions. The privacy issue is particularly critical for analysing trajectory semantic rich data, therefore MASTER consortium is assisted by the Independent Ethics Advisor Prof. Bettina Berendt and an Ethical Committee.

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