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Cineca received the BDVA i-Space Silver label. The Board of Directors of the Big Data Value Association, BDVA, followed the recommendation of the i-Space review committee and decided to grant the BDVA i-Space Silver label to Cineca. As a labelled i-Space Cineca is recognized as a data innovation space to contribute driving forward the Big Data Value ecosystem and its Digital Agenda for the benefit of Europe, its Companies and Citizens. The grant has been announced during the European Big Data Value Forum. During the event Cineca presented the i-Spaces at the session “Data Sharing/Integration”

Data Ecosystems
i-spaces are Data Innovation Spaces. As Trusted Data Ecosystems, they target to accelerate take up of data driven innovation in private sectors like Manufacturing 4.0, Logistics, e Commerce, Media, Aerospace, Automobile, Energy, Agriculture and Agroindustry, Pharma; as well as in non-profit sectors (e-Government, Environment, Public Health, Smart Cities). i-Spaces provide services to enable and support the development and validation of new Big Data use cases, i-Spaces therefore generate economical, societal and environmental value to their local ecosystems.

Data Innovation Hub
Labelled i-Spaces, beyond exchange of good practice, form a Europe-wide ecosystem, in order to foster trans-boundaries data innovation. In the concept of Digital Innovation Hubs (DIH), i-Spaces act as the nucleus with their competences, infrastructure, expertise and access to regional communities. Their tailored SME support through a wide range of flexible services enables them to support a wide range of digitization strategies.

Cineca was awarded the i-Space Label thanks to the development of many projects in the field of Big Data in different areas: bioinformatics (such as Elixir, and Telethon), Industry 4.0, (in the context of Fortissimo) and other areas (such as iMediaCities projects, sentiment analysis for the Royal Palace of Caserta, ISTAT, RAI, Unipol). For the year 2018 new innovation projects are underway, which include, among other things, the development / integration of Machine Learning algorithms for predictive maintenance, and the creation of platforms for the preservation, sharing, and analysis of heterogeneous data.