EUBra-BIGSEA · Project

Cloud Platform That Processes Petabytes of Data With Built-In Privacy and Quality Controls

digitalTestedTRL 5Thin data (2/5)

Imagine you have mountains of data — petabytes of it — coming from millions of connected devices and people, and you need answers fast without compromising anyone's privacy. This EU-Brazil collaboration built cloud services that can crunch massive datasets while automatically adjusting computing power to meet deadlines and budgets. Think of it like a smart electricity grid for data processing: it scales up when demand spikes and scales down when it doesn't, all while keeping sensitive information locked behind the right borders. They even built a working "Routes for People" app to prove it works in the real world.

By the numbers

PB-scale

Data processing capacity (petabytes)

Consortium partners

Countries involved (ES, IT, PT, UK)

EUR 1,499,625

EU contribution

Total deliverables produced

Demo deliverables including a live web application

The business problem

What needed solving

Companies processing massive data volumes face three simultaneous headaches: unpredictable costs when workloads spike, compliance risks when sensitive data crosses borders, and performance failures when standard tools like MapReduce cannot handle real-time streaming. Most off-the-shelf cloud solutions force you to choose between speed, privacy, and cost — you rarely get all three.

The solution

What was built

The project delivered a working "Routes for People" web application for mobility data analysis, security and privacy extensions for big data ecosystems, and QoS extensions that dynamically adjust cloud resources at the PaaS level. In total, 31 deliverables were produced including 3 demonstrated applications.

Audience

Who needs this

City transit authorities processing millions of daily passenger data pointsCloud service providers needing smarter SLA management for big data workloadsFinancial institutions with cross-border data residency requirementsLarge retailers analyzing petabyte-scale customer behavior dataGovernment agencies federating datasets across national boundaries

Business applications

Who can put this to work

Public Transportation & Smart Cities

enterprise

Target: City transit authorities and mobility-as-a-service providers

If you are a transit authority dealing with millions of daily passenger movements and struggling to optimize routes in real time — this project developed a 'Routes for People' application that processes massive mobility datasets in the cloud. It was built as a working web application during the project and demonstrates how big data analytics can improve route planning for connected urban populations.

Cloud Computing & IT Services

mid-size

Target: Cloud platform providers and managed service companies

If you are a cloud service provider struggling to guarantee performance when clients run unpredictable big data workloads — this project developed QoS extensions that dynamically adjust resources both vertically and horizontally to meet deadlines. The system includes price-based dynamic rescheduling so you can offer competitive SLAs while optimizing your infrastructure costs across a 6-partner, 4-country federated setup.

Financial Services & Insurance

enterprise

Target: Banks and insurers handling cross-border data processing

If you are a financial institution dealing with petabyte-scale transaction data that must comply with strict data residency rules across borders — this project built security and privacy extensions for big data ecosystems. The SLA management specifically addresses boundaries of where protected data can be moved, letting you process data across EU and international cloud infrastructure while meeting regulatory requirements.

Frequently asked

Quick answers

What would it cost to implement this kind of big data cloud platform?

The project received EUR 1,499,625 in EU funding over 2 years with 6 partners. Actual licensing or deployment costs are not specified in the project data. You would need to contact the coordinator at Universitat Politecnica de Valencia to discuss terms for accessing the platform components.

Can this handle industrial-scale data volumes?

Yes — the project was explicitly designed to handle petabyte-scale data processing. The objective specifically mentions 'capturing, federating and annotating on the order of PB of data' with both batch and real-time streaming capabilities beyond standard MapReduce.

What about intellectual property and licensing?

The project was coordinated by Universitat Politecnica de Valencia with 6 partners across 4 countries (Spain, Italy, Portugal, UK). As an EU-funded RIA project, IP arrangements follow Horizon 2020 rules. Specific licensing terms would need to be negotiated with the consortium.

Does this work with existing cloud infrastructure or require a proprietary setup?

The platform was built with interoperability and international standardization as explicit goals. The QoS extensions operate at the PaaS level, suggesting integration with existing cloud infrastructure rather than replacement. The EU-Brazil federation aspect demonstrates cross-platform compatibility.

How mature is the security and privacy component?

The project delivered dedicated security and privacy extensions integrated into the big data ecosystem (documented in deliverable reports). The SLA system specifically manages data movement boundaries for protected data, which is relevant for GDPR-type compliance scenarios.

What is the timeline from evaluation to deployment?

The project ran from January 2016 to December 2017 and is now closed. The 'Routes for People' application was deployed as a running web application accessible through the internet. Based on available project data, additional development would be needed to adapt the platform to a specific commercial use case.

Consortium

Who built it

The consortium of 6 partners across 4 countries (Spain, Italy, Portugal, UK) is research-heavy: 3 universities, 2 research organizations, and just 1 industry partner. The industry ratio is only 17% with 1 SME, which means the technology was developed primarily in academic settings. Universitat Politecnica de Valencia in Spain led the coordination. For a business looking to adopt this technology, the low industry involvement means you would likely need to invest in adapting the research outputs for production environments. The international spread (EU-Brazil collaboration) does suggest the platform was tested across geographically distributed infrastructure, which is a plus for companies needing cross-border data processing.

UNIVERSITAT POLITECNICA DE VALENCIACoordinator · ES
UNIVERSIDADE DE COIMBRAparticipant · PT
POLITECNICO DI MILANOparticipant · IT
TRUST-IT SERVICES LIMITEDparticipant · UK
FONDAZIONE CENTRO EURO-MEDITERRANEOSUI CAMBIAMENTI CLIMATICIparticipant · IT
BARCELONA SUPERCOMPUTING CENTER CENTRO NACIONAL DE SUPERCOMPUTACIONparticipant · ES

How to reach the team

Universitat Politecnica de Valencia, Spain — contact through SciTransfer for a warm introduction to the research team

Order a report on this consortium See UNIVERSITAT POLITECNICA DE VALENCIA profile →

Next steps

Talk to the team behind this work.

Want to explore how petabyte-scale cloud data processing with built-in privacy controls could work for your business? SciTransfer can connect you directly with the research team and help evaluate fit for your use case.

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