Data interoperability refers to the functionality of information systems to exchange data and enable information sharing.  More specifically, itis defined as the ability of systems and services that create, exchange, and consume data to have clear, shared expectations for the format, contents, context, and meaning of that data. Thus, it allows to access and process data from multiple sources in diverse formats without losing sense and then to integrate that data for mapping, visualization, and other forms of representation and analysis. Data interoperability enables people to find, explore, and understand the structure and content of heterogeneous data.

In this context, SEDIMARK aims to provide an enriched secure decentralized data and services marketplace where scattered data from various domains and geographical locations within the EU can be easily generated, cleaned, protected, discovered, and enriched with metadata, AI and analytics, and exploited for diverse business and research scenarios. SEDIMARK involves a combination of heterogeneous data, and achieving data interoperability will allow it to maximize the value of the data and overcome the significant challenges posed by distributed assets (heterogeneity, data formats, sources, etc). For this to happen, SEDIMARK will reuse the semantic models developed in previous and ongoing EU initiatives, such as Gaia-X, IDS and NGSI-LD, and propose extensions to them to create one generic semantic model able to annotate and enrich heterogeneous data from multiple domains semantically.

Besides data, interoperability between AI models that emerge from this data is of great interest. In the decentralized environment of SEDIMARK, decentralized training requires that users train their models locally and then exchange model weights for jointly learning a global model. Ensuring that all SEDIMARK users will use the exact same machine learning platform for training the model and the exact same machines is unrealistic. So, SEDIMARK models will be agnostic to underlying platforms and SEDIMARK will provide tools to convert models to various formats and support models to run on machines of various capabilities and on various platforms.

ARTEMIS is the product of WINGS that is oriented to the proactive management of water, energy, gas infrastructures.

Based on the WINGS approach, it combines advanced technologies (IoT, AI, advanced networks and visualizations) with domain knowledge, to address diverse use cases. Being a management system it delivers the following functionalities.

• Efficient metering: optimized information flow and cost with 24/7 capability, prediction of demand and of capabilities);
• Fault management: faulty meters, predictive maintenance, outage handling (energy), leakage or flood avoidance (water), outage handling.
• Performance optimizations: optimization of water quality, maximization of revenue water, optimization of the deployment of renewables and of storage components, optimization for residences / businesses factories.
• Configuration and security aspects.

Commercial traction has been achieved, while further interest is stimulated in various areas and with various tentative partners.

In parallel WINGS strives to develop and integrate further advances.  A wave of new projects related to ARTEMIS activities is being implemented. SEDIMARK aims to create a secure decentralised data marketplace based on distributed ledger technology and AI. Under this new approach,

• Data will no longer be stored on the “core cloud” but also on “edge systems”, close to where they are generated, thus avoiding security concerns.
• According to diverse strategies, data will be “cleaned”, labelled and classified, in accordance with legal / ethical frameworks and FAIR (findable, accessible, interoperable and reusable) principles, for enabling easy linkage and efficient utilization.
• Diverse analysis mechanisms can be powered.

Within SEDIMARK, WINGS contributes on the marketplace (leveraging its experience in other vertical sectors, like food security and safety) and with AI strategies.

SEDIMARK will empower European stakeholders to set the proper foundation for the energy market, expand their competences and compete and scale at a global level

This document is a deliverable of the SEDIMARK project, funded by the European Commission under its Horizon Europe Framework Programme. This document presents the “D6.2 Dissemination and exploitation plan” deliverable, including the expected impact of the ongoing and planned activities, target audience, milestones, and mechanisms to assess the dissemination and exploitation activities carried out throughout the project execution.
Dissemination activities are any action related with the public disclosure of the project results by any appropriate means, including scientific publications. On the other hand, Communication activities also include the promotion of the project itself to multiple audiences, including both the media and the public. Separating the concept and the goal of dissemination and communication plan is important as the communication plan is about the project and its results, whilst the dissemination one is only about the results.
Moreover, exploitation activities have a broader scope compared to communication and dissemination. They can include actions such as utilizing the project results in further research activities other than those covered by the concerned project, developing, creating and marketing a product or process, creating and providing a service, or even in standardisation activities.

SEDIMARK knows the importance of regulating data management issues within a context such as the one posed by the project. A solution will be considered where consortium partners will deposit all underlying information on data-related business processes (data storage, data provisioning, processing etc.) of the SEDIMARK solution clearly and transparently.
The purpose of the Data Management Action Plan (DMAP) is to identify the main data management elements that apply to the SEDIMARK project and the consortium. This document is the first version of the DMAP and will be reviewed as soon as there is a clearer understanding of the types of data that will be collected.
Given the wide range of sources from which data will be collected or become available within the project, this document outlines that the consortium partners will consider embracing and applying the Guidelines on FAIR Data Management in Horizon 2020 and Horizon Europe (HE); “In general terms your data should be ‘FAIR’, that is Findable, Accessible, Interoperable and Re-usable”, as information about data to be collected becomes clearer”.

As the name suggest, SEDIMARK will be a Data and Service marketplace. But SEDIMARK focus is not only on data and services assets: Decentralisation also play a key role…

D as Decentralisation

The decentralisation allows to stay away from a single and central authority for control and decision-making, instead it enables the interactions directly among multiple independent parties.

There are several perks in a decentralised system:

• Reduced Weakness: relying too much on one entity can lead to systemic failures. Multiple entities shield from unfortunate events.
• Optimization of resources: in a decentralized system, the resources available can be spread among multiple entities to provide better services.
• Security and Trust: in a decentralized network, security and trust is a must pre-condition.

SEDIMARK Marketplace achieves the security and trust thanks to Distributed Ledger Technologies (DLT).

D as DLT

A DLT is a network composed of several nodes that independently replicate, share, and synchronize the same data spread across many different physical locations without a central administrator.

The most famous example is the Blockchain, today largely employed for financial transactions with bitcoin crypto-currency. However, the SEDIMARK decentralised architecture will be based on a different DLT, that is the IOTA Tangle designed and deployed by the IOTA Foundation. The IOTA Tangle is an open, feeless and highly scalable distributed ledger, designed to support both data and value transfer with a green fashion.

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Machine Learning introduction

Machine Learning (ML) is a modern and efficient branch of AI (Artificial Intelligence), specialised in pattern recognition within data streams. It can provide precise analysis based on statistics to detect insights from a large data set, using the same principle as human neural networks in our brain. Every system equipped with ML must learn and discover patterns from historical data and compare its predictions with real data, before providing reliable information. That is why AI systems are trained with as much data as possible.

ML algorithms are more efficient than traditional modelling methods and can surpass human intelligence through its powerful computational efficiency. For instance, image recognition and time series analysis are well-known and widespread domains of application of ML for real world cases, such as the EU-funded SEDIMARK project. SEDIMARK aims at building a secure, trusted and intelligent decentralised data and services marketplace over several years, using ML in order to automate data quality management. Over time, the project results will provide ever-increasing accuracy and precision with its growing data sources.

ML could be directly used on edge systems to ensure data quality. Some algorithms are specialised for this purpose, with low power consumption and modest memory size. For instance, EdgeML and TinyML are open-source libraries that provide this kind of outcome.

ML embarked on edge systems

The IoT platform from EGM (i.e. the EdgeSpot) is compatible with both libraries and could manage and distribute FAIR data in an energy efficient way. The ONNX (Open Neural Network Exchange), an open format representing ML models, may be a solution to select the right combinations of tools. And finally, with the help of the use cases provided within SEDIMARK, the project might elaborate a concrete strategy to automate and manage data quality.

SEDIMARK plans to build a distributed registry of resources stored on edge systems, close to where data is generated. The purpose is to clean, label, validate and anonymise data.

A digital twin is at the highest level, an architectural construct that is enabled by a combination of technology streams such as IoT (Internet of Things), Cloud Computing, Edge Computing, Fog Computing, Artificial Intelligence, Robotics, Machine Learning, and Big Data Analytics.

The concept of a Digital Twin is based on the fact that every physical part has a virtual counterpart that is conceptual, structural and functional the same as the physical part. The concept of Digital Twins dates in the 1970s used by NASA in the Apollo 13 mission. Nowadays the Digital Twins are used in various industries, being a key concept in realizing the communication mechanism between the physical and the virtual world by using data.

The primary use case for Digital Twin is asset performance, utilization, and optimization. Digital Twin enables monitoring, diagnosing, and prognostics capabilities for a particular use case.

Some applications for Digital Twin are presented below:

Digital Twin for creating 3D modeling of digital objects from physical objects. This use case is a critical success factor for smart manufacturing initiatives.

Digital Twin is used inside factories to identify symptoms with constant monitoring and finding the root causes of production issues.

In healthcare Digital Twin are used for simulation purposes so doctors can do risky operations first in a simulated environment before doing the operation on a real patient.

Town planners use Digital Twin initiatives by using virtual models to improve the city conditions in a proactive manner. This approach can reduce the complexity and simplify the processes for planners. In conclusion using the Digital Twin architecture can help in a lot of industries making the work easier.

A digital twin is at its highest level, an architectural construct that is enabled by a combination of technology streams such as IoT (Internet of Things), Cloud Computing, Edge Computing, Fog Computing, Artificial Intelligence, Robotics, Machine Learning and Big Data. Analytics.

The digital twin concept is based on the fact that every physical part has a virtual part that is conceptually, structurally and functionally the same as the physical part. The concept of Digital Twins dates to the 1970s, used by NASA in the Apollo 13 mission. Nowadays Digital Twins are used in various industries, being a key concept in realizing the communication mechanism between the physical and virtual worlds using data.

The primary use case for Digital Twin is asset performance, utilization, and optimization. Digital Twin enables monitoring, diagnostic and forecasting capabilities for a specific use case.

Examples of Digital Twin application scenarios are described in the following:

• Digital Twin for creating 3D modelling of digital objects from physical objects. This use case is a critical success factor for smart manufacturing initiatives.
• Digital Twin is used in factories to identify symptoms with constant monitoring and find the root causes of production problems.
• In healthcare, Digital Twin is used for simulation purposes so that doctors can perform risky operations first in a simulated environment before performing the operation on a real patient.
• Urban planners use Digital Twin initiatives using virtual models to improve city conditions in a proactive manner. This approach can reduce complexity and simplify processes for planners.

Digital Twins and Data Spaces

Digital twins must be considered in their relationship to data spaces. A larger overview is therefore required, including systemic oversight, and supporting infrastructure.

International Data Spaces (IDS) provides data space technologies and concepts for various application domains that enable standardized data exchange and integration in a trusted environment. The International Data Spaces Association (IDSA) is a non-profit organization that promotes IDS architecture as an international standard in a variety of fields, including healthcare, mobility, agriculture, and more.

It is expected that in the medium term, in strong relation to specific requirements, collaboration solutions with centralized data storage in one or more clouds and distributed data storage with efficient data processing will be realized by combining the Digital Twins with Dataspaces.

With help of the use-cases provided within SEDIMARK, the project could elaborate a concrete strategy in which this relationship between Digital Twins and Dataspaces can prove of real value.

Digital solutions are important to the energy industry because they can help the energy system become more flexible, reliable, and efficient while also making it possible to incorporate renewable energy sources.

Because they can make it possible to share and analyse large amounts of data from a variety of sources, such as data on energy production and consumption from smart meters, weather data, and information about the grid, Data Marketplaces may be especially useful for the energy industry. Energy storage, grid management, and the integration of renewable energy sources can all benefit from a better understanding and management of the energy system by energy companies and grid operators. New business models like peer-to-peer energy trading and the integration of electric vehicles into the energy system can also be made possible by Data Spaces.

By unlocking the value of data and ensuring that data can be shared and reused across various industries, the European Commission wants to create a society and economy driven by data, according to the European Strategy for Data. Additionally, the strategy emphasizes the significance of data in achieving the EU's energy and climate goals.

The Green Deal is a plan by the European Commission to make the EU's economy sustainable by turning climate and environmental problems into opportunities in all sectors and making the transition fair and inclusive for everyone. It includes smart grid and metering systems, digital platforms for sharing and analysing energy data, and integrating distributed energy resources like electric vehicles and small-scale renewable energy production.

However, the question of how to apply these ideas to the current energy landscape is still unanswered. For instance, how can we guarantee that frameworks for data governance and management are in place to facilitate data access while safeguarding privacy and security? In terms of the integration of renewable energy sources and the creation of novel business models, how can we encourage the development and implementation of digital technologies and data solutions in the energy sector? And how can we make sure that the benefits of digitalization are available to everyone, especially in terms of reducing energy poverty and ensuring access to energy? To fully realize the potential of digital technologies and data for the energy sector, these are some of the crucial questions that must be answered and SEDIMARK will try to find the way to contribute to solve at least some of them. Get aboard the SEDIMARK cruise and share with us the experience!

image: ipopba | Getty Images