The first Horizon 2020 projects belonging to the PPP have started, tackling different aspects of
the Big Data Value Universe.
EDI provides an incubation programme and up to €100k equity free for EU Big Data startups sorting out data challenges from companies in a myriad of sectors. EDI will help Big Data entrepreneurs with a free infrastructure with open source tools, training, support, business services to develop their business idea, and a equity-free funding.
DATA BIO is the deployment of a global Big Data Platform benefiting the raw material production from agriculture, forestry and fishery-aquaculture for the bio-economy industry.
Overcoming traditional fragmentation in maritime-related industries by creating a new data-driven value chain which demonstrates and exploits economic, societal and environmental impacts.
Development of robust technologies, including a control dashboard to be applied in the acquisition of user consents and workflows of data, addressing the contradiction between Big Data Innovation and privacy-aware Data policies.
A self-service solution, aiming to empower users to easily utilize and interact with big data technologies by designing, building and demonstrating a unified framework that significantly increases the speed of data analysis.
Curating the Fashion Data Universe to enhance buyer’s experience and provide retailers with content to build stories around its products. The FashionBrain project aims at combining data from different sources to support different fashion industry players by predicting upcoming fashion trends from social media.
Involving the full Big Data value chain and its stakeholders into a common ethical approach to Big Data processing, ultimately enhancing the confidence of citizens towards these technologies and markets.
BigDataStack is a data-centric view of the Cloud and will provide a complete infrastructure management system that will base the management and deployment decisions on data aspects thus being fully scalable, runtime adaptable and high-performing.
TheyBuyForYou will explore how procurement knowledge graphs, paired with data management, analytics and interaction design could be used to reform four key procurement areas: economic development, demand management, competitive markets and supplier intelligence.
Delivering a Big Data IaaS platform to support a multilingual, cross-sector value chain on Public Safety and Personal Security. AEGIS will help EU companies to adopt a more data-driven mentality, extending and/or modifying their individual data solutions and offering more advanced data services.
Building an SME-focused Ecosystem around Big Data by gathering corporations, mentors, expertise, technology and, of course, Data.
BigDataGrapes aims to help European companies in the wine and natural cosmetics industries become more competitive in the international markets; specifically tries to support business decisions with real-time and cross-stream analysis of very large, diverse and multimodal data sources.
A one-stop shop for a discovery of native-aviation, extra-aviation and derivative-aviation data assets; a trusted analytics sandbox
to link and gain insights into private, confidential and external data; a data brokerage platform enabling new forms of data sharing based on P2P licensing schemes.
Making transport and mobility smarter across European cities, exploiting cross-sectorial Big Data and incorporating the Human Factor.
Providing an industry-validated methodology and integrated technical offering for designing, developing, querying, evolving, analysing and monitoring scalable hybrid data persistence architectures that will meet the growing scalability and heterogeneity requirements of the European industry.
CLASS works towards an efficient distribution of big data workloads along the compute continuum (from edge to cloud) in a completely transparent way while providing sound real-time guarantees on end-to-end data analytics responses. The capabilities of the CLASS framework will be demonstrated on a real smart-city use case in the City of Modena.
The objective of Cross-CPP is to establish an IT environment offering data streams coming from various industrial sectors (vehicle, smart home devices, etc.). Providing access to these Cross Industrial Data streams will enable to build new and innovative business ideas for many stakeholders.
BigMedilytics will transform Europe’s Healthcare sector by using state-of-the-art Big Data technologies to achieve breakthrough productivity in the sector by reducing cost, improving patient outcomes and delivering better access to healthcare facilities simultaneously.
BodyPass ambition is to create a platform to exchange of 3D Data from different sectors as healthcare and consumer goods sectors. This project will offer tools that open new opportunities for solving business and social challenges, braking barriers between the health sector and consumer goods sector and eliminate the current data silos.
Humanities and cultural data are often difficult to digitise and include in the Big Data Value chain. What are the risks of letting them out? Which is the real gap between Big Data and knowledge?
Effectively combining in a consortium Large Enterprises, SMEs and Academia this project provides coordination and support for the current and future H2020 projects within the BDV PPP, fostering a truly vibrant community around Big Data and facilitating the common bodies to discover and exploit synergies.
SLIPO develops software, models and processes for transforming conventional POI formats and schemas into RDF data, interlinking POI entities from different datasets, enriching POI entities with additional metadata, assessing the quality of the integrated POI data and offering value-added services.
Researching, developing and exploiting a new software framework that aims at increasing the efficiency of Big Data. This will be applied in the transport, mobility, motor insurance and health sectors.
Developing a true information marketplace which empowers patients as primary owners of their personal data, leveraging its value within a trusted relationship between citizens, hospitals research centres and businesses.
Building a Legal Knowledge Graph of legislation, case law, standards and industry norms in order to deliver smart services for compliance in multilingual Europe.
The FANDANGO project aims at providing unified techniques and an integrated big data platform to support traditional media industries to face the new “data” economy with a better transparency to the citizens under a Responsible, Research and Innovation (RRI) prism.
A platform to integrate customer insights from different languages and countries along with other indirect data such as the weather or some events to enrich the shoppers’ journey.
Evidence-Based Big Data Benchmarking to Improve Business Performance. Organisations rely on evidence from the Benchmarking domain to provide answers to how their processes are performing. There is extensive information on how and why to perform technical benchmarks for the specific management and analytics processes.
Scalable Policy-aware Linked Data Architecture for Privacy, Transparency and Compliance. Allowing citizens and organisations to share more data, while guaranteeing data protection compliance, thus enabling both trust and the creation of valuable new insights from shared data.
E2Data proposes an end-to-end solution for Big Data deployments that will fully exploit and advance the state-of-the-art in infrastructure services by delivering a performance increase of up to 10x while utilizing up to 50% fewer cloud resources.
47 leading transport, logistics and IT actors aiming to show concrete, measurable and verifiable evidence of Big Data Value for mobility and logistics.
The biggest European initiative in Big Data for Industry 4.0, will lead the construction of the European Industrial Data Space to improve the competitiveness of Industry 4.0 and will guide the European manufacturing industry in the introduction of Big Data in the factory, providing the industrial sector with the necessary tools to obtain the maximum benefit of Big Data.