DITAS · Project

One Platform to Manage Data Across Cloud and Edge Devices Without Complexity

digitalTestedTRL 5

Imagine you have sensors in a factory, cameras at a warehouse, and databases in the cloud — all producing data, but none of them talking to each other smoothly. DITAS built a kind of smart middleman that automatically decides where to process your data: close to the source (on the factory floor) when speed matters, or up in the cloud when you need heavy-duty analysis. Developers just describe what data quality and security they need, and the system figures out the rest. Think of it like a GPS for your data — it finds the best route depending on traffic conditions.

By the numbers

consortium partners

countries represented

industrial partners in consortium

total project deliverables

55%

industry ratio in consortium

The business problem

What needed solving

Companies running data-intensive operations across multiple locations — factories, hospitals, logistics hubs — face a painful choice: process data locally (fast but unreliable) or send everything to the cloud (reliable but slow and expensive). Most end up with a messy patchwork of custom integrations that break every time they add a new sensor or data source. They need a single platform that handles the where-and-how of data processing automatically.

The solution

What was built

The team built an SDK and execution environment for mixed cloud/edge data processing. Key deliverables include a fully integrated execution environment prototype (final release) with data monitoring, movement selection, and analytics capabilities, plus a first-release prototype showing the iterative development path. In total, the project produced 15 deliverables over 3 years.

Audience

Who needs this

Industrial IoT platform operators managing sensor data from factory floorsDigital health companies processing patient data from wearable devices under strict privacy rulesSmart city platform providers integrating data from thousands of distributed sensorsLogistics companies with edge computing needs at warehouses and distribution centersCloud service providers looking to extend their offerings to fog/edge computing

Business applications

Who can put this to work

Manufacturing & Industrial IoT

enterprise

Target: Manufacturers with sensor networks and production line monitoring

If you are a manufacturer dealing with hundreds of sensors on the factory floor generating data faster than your cloud can process it — this project developed an execution environment that decides in real time whether to crunch numbers locally at the edge or send them to the cloud. Your quality control alerts arrive faster, and you stop paying to upload raw data you never needed in the cloud.

Healthcare & Remote Patient Monitoring

mid-size

Target: Digital health companies managing patient data from wearable devices

If you are a digital health company dealing with sensitive patient data flowing from wearable devices to hospitals to cloud analytics — this project developed Virtual Data Containers that let you set strict privacy and compliance rules once, and the system automatically keeps sensitive data local while sending anonymized summaries to the cloud. You reduce compliance risk without rebuilding your entire data pipeline.

Smart Cities & Utilities

enterprise

Target: Utility companies or city platform operators managing distributed sensor networks

If you are a utility company dealing with smart meter data scattered across thousands of locations — this project developed a platform that virtualizes all those data sources into a single view for your developers. They write code once, and the system handles where data gets stored, processed, and delivered — even as the network of devices changes. You cut integration time for new data sources significantly.

Frequently asked

Quick answers

What would it cost to adopt this technology?

The project was publicly funded EU research (RIA), so the core platform and SDK are research outputs. Licensing terms would need to be negotiated with the consortium, led by Atos Spain. With 6 industrial partners in the consortium, there may already be commercialization pathways or spin-off products available.

Can this work at industrial scale with thousands of devices?

The project specifically targeted data-intensive applications dealing with distributed and heterogeneous data sources including smart devices, sensor networks, and traditional servers. The final release of the execution environment prototype integrated data movement and analytics components. However, large-scale production deployment would likely require additional engineering beyond the research prototype.

Who owns the intellectual property and can I license it?

As an RIA project coordinated by Atos Spain SA with 11 partners across 6 countries, IP is typically shared among consortium members according to their grant agreement. Contact Atos Spain or check the project website for licensing options and technology transfer opportunities.

How does this handle data privacy and compliance like GDPR?

The project explicitly addressed security and compliance as core objectives. Virtual Data Containers allow developers to express requirements on data in terms of security and privacy, and the execution environment handles data processing and delivery according to those rules. This design-by-policy approach aligns well with GDPR principles.

How long would it take to integrate this into our existing systems?

DITAS provides both an SDK for development and an execution environment for deployment. The SDK approach means your developers can work with familiar tools while the platform handles the complexity of mixed cloud and edge infrastructure underneath. Based on available project data, the project ran for 3 years and produced 15 deliverables including the final execution environment prototype.

Is this just research or is there something we can actually test?

The consortium produced a working execution environment prototype with a first release and a final release that fully integrated data movement and analytics components. This is a functional prototype, not just a concept paper. However, it was built as a research demonstrator, not a commercial product.

Consortium

Who built it

The DITAS consortium is notably industry-heavy at 55%, with 6 industrial partners out of 11 total — a strong signal that the technology was designed with real-world use in mind, not just academic interest. The project is coordinated by Atos Spain, a major IT services company with global reach, which increases the likelihood of technology surviving beyond the grant period. The consortium spans 6 countries (Spain, Germany, Italy, Israel, Greece, Switzerland), giving it a pan-European perspective on data regulation and infrastructure diversity. With 2 universities and 3 research organizations backing the industrial partners, the science-to-engineering pipeline was well-resourced across the project's 15 deliverables.

ATOS SPAIN SACoordinator · ES
CLOUDSIGMA AGparticipant · CH
TECHNISCHE UNIVERSITAT BERLINparticipant · DE
IDEKO S COOPparticipant · ES
FONDAZIONE CENTRO SAN RAFFAELEthirdparty · IT
OSPEDALE SAN RAFFAELE SRLparticipant · IT
POLITECNICO DI MILANOparticipant · IT
ATOS CONSULTING CANARIAS SA UNIPERSONALthirdparty · ES
ATOS IT SOLUTIONS AND SERVICES IBERIA SLthirdparty · ES
EREVNITIKO PANEPISTIMIAKO INSTITOUTO SYSTIMATON EPIKOINONION KAI YPOLOGISTONparticipant · EL
IBM ISRAEL - SCIENCE AND TECHNOLOGY LTDparticipant · IL

How to reach the team

Atos Spain SA coordinated this project — reach out to their innovation or R&D partnerships division for licensing and technology transfer discussions.

Order a report on this consortium See ATOS SPAIN SA profile →

Next steps

Talk to the team behind this work.

Want an introduction to the DITAS team? SciTransfer can connect you with the right technical contact at Atos Spain and help you evaluate whether this platform fits your data infrastructure needs.

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