An adequate compute and storage infrastructure supporting the full exploitation of Copernicus and Earth Observation datasets is currently not available in Europe.This paper presents the cross-disciplinary open-source ...An adequate compute and storage infrastructure supporting the full exploitation of Copernicus and Earth Observation datasets is currently not available in Europe.This paper presents the cross-disciplinary open-source technologies being leveraged in the C-SCALE project to develop an open federation of compute and data providers as an alternative to monolithic infrastructures for processing and analysing Copernicus and Earth Observation data.Three critical aspects of the federation and the chosen technologies are elaborated upon:(1)federated data discovery,(2)federated access and(3)software distribution.With these technologies the open federation aims to provide homogenous access to resources,thereby enabling its users to generate meaningful results quickly and easily.This will be achieved by abstracting the complexity of infrastructure resource access provisioning and orchestration,including discovery of data across distributed archives,away from the end-users.Which is needed because end-users wish to focus on analysing ready-to-use data products and models rather than spending their time on the setup and maintenance of complex and heterogeneous IT infrastructures.The open federation will support processing and analysing the vast amounts of Copernicus and Earth Observation data that are critical for the implementation of the Destination Earth resp.Digital Twins vision for a high precision digital model of the Earth to model,monitor and simulate natural phenomena and related human activities.展开更多
With the increasing global mobile data traffic and daily user engagement,technologies,such as mobile crowdsensing,benefit hugely from the constant data flows from smartphone and IoT owners.However,the device users,as ...With the increasing global mobile data traffic and daily user engagement,technologies,such as mobile crowdsensing,benefit hugely from the constant data flows from smartphone and IoT owners.However,the device users,as data owners,urgently require a secure and fair marketplace to negotiate with the data consumers.In this paper,we introduce a novel federated data acquisition market that consists of a group of local data aggregators(LDAs);a number of data owners;and,one data union to coordinate the data trade with the data consumers.Data consumers offer each data owner an individual price to stimulate participation.The mobile data owners naturally cooperate to gossip about individual prices with each other,which also leads to price fluctuation.It is challenging to analyse the interactions among the data owners and the data consumers using traditional game theory due to the complex price dynamics in a large-scale heterogeneous data acquisition scenario.Hence,we propose a data pricing strategy based on mean-field game(MFG)theory to model the data owners’cost considering the price dynamics.We then investigate the interactions among the LDAs by using the distribution of price,namely the mean-field term.A numerical method is used to solve the proposed pricing strategy.The evaluations demonstrate that the proposed pricing strategy efficiently allows the data owners from multiple LDAs to reach an equilibrium on data quantity to sell regarding the current individual price scheme.The result further demonstrates that the influential LDAs determine the final price distribution.Last but not least,it shows that cooperation among mobile data owners leads to optimal social welfare even with the additional cost of information exchange.展开更多
The incompleteness of patient health data is a threat to the management of COVID-19 in Africa and globally.This has become particularly clear with the recent emergence of new variants of concern.The Virus Outbreak Dat...The incompleteness of patient health data is a threat to the management of COVID-19 in Africa and globally.This has become particularly clear with the recent emergence of new variants of concern.The Virus Outbreak Data Network(VODAN)-Africa has studied the curation of patient health data in selected African countries and identified that health information flows often do not involve the use of health data at the point of care,which renders data production largely meaningless to those producing it.This modus operandi leads to disfranchisement over the control of health data,which is extracted to be processed elsewhere.In response to this problem,VODAN-Africa studied whether or not a design that makes local ownership and repositing of data central to the data curation process,would have a greater chance of being adopted.The design team based their work on the legal requirements of the European Union’s General Data Protection Regulation(GDPR);the FAIR Guidelines on curating data as Findable,Accessible(under well-defined conditions),Interoperable and Reusable(FAIR);and national regulations applying in the context where the data is produced.The study concluded that the visiting of data curated as machine actionable and reposited in the locale where the data is produced and renders services has great potential for access to a wider variety of data.A condition of such innovation is that the innovation team is intradisciplinary,involving stakeholders and experts from all of the places where the innovation is designed,and employs a methodology of co-creation and capacity-building.展开更多
The field of health data management poses unique challenges in relation to data ownership, the privacy of data subjects, and the reusability of data. The FAIR Guidelines have been developed to address these challenges...The field of health data management poses unique challenges in relation to data ownership, the privacy of data subjects, and the reusability of data. The FAIR Guidelines have been developed to address these challenges. The Virus Outbreak Data Network(VODAN) architecture builds on these principles, using the European Union’s General Data Protection Regulation(GDPR) framework to ensure compliance with local data regulations, while using information knowledge management concepts to further improve data provenance and interoperability. In this article we provide an overview of the terminology used in the field of FAIR data management, with a specific focus on FAIR compliant health information management, as implemented in the VODAN architecture.展开更多
基金the C-SCALE project(https://c-scale.eu/),which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101017529。
文摘An adequate compute and storage infrastructure supporting the full exploitation of Copernicus and Earth Observation datasets is currently not available in Europe.This paper presents the cross-disciplinary open-source technologies being leveraged in the C-SCALE project to develop an open federation of compute and data providers as an alternative to monolithic infrastructures for processing and analysing Copernicus and Earth Observation data.Three critical aspects of the federation and the chosen technologies are elaborated upon:(1)federated data discovery,(2)federated access and(3)software distribution.With these technologies the open federation aims to provide homogenous access to resources,thereby enabling its users to generate meaningful results quickly and easily.This will be achieved by abstracting the complexity of infrastructure resource access provisioning and orchestration,including discovery of data across distributed archives,away from the end-users.Which is needed because end-users wish to focus on analysing ready-to-use data products and models rather than spending their time on the setup and maintenance of complex and heterogeneous IT infrastructures.The open federation will support processing and analysing the vast amounts of Copernicus and Earth Observation data that are critical for the implementation of the Destination Earth resp.Digital Twins vision for a high precision digital model of the Earth to model,monitor and simulate natural phenomena and related human activities.
基金supported within the project TRACE-V2Xfunding from the European Union’s HORIZON-MSCA-2022-SE-01-01 under grant agreement(101131204).
文摘With the increasing global mobile data traffic and daily user engagement,technologies,such as mobile crowdsensing,benefit hugely from the constant data flows from smartphone and IoT owners.However,the device users,as data owners,urgently require a secure and fair marketplace to negotiate with the data consumers.In this paper,we introduce a novel federated data acquisition market that consists of a group of local data aggregators(LDAs);a number of data owners;and,one data union to coordinate the data trade with the data consumers.Data consumers offer each data owner an individual price to stimulate participation.The mobile data owners naturally cooperate to gossip about individual prices with each other,which also leads to price fluctuation.It is challenging to analyse the interactions among the data owners and the data consumers using traditional game theory due to the complex price dynamics in a large-scale heterogeneous data acquisition scenario.Hence,we propose a data pricing strategy based on mean-field game(MFG)theory to model the data owners’cost considering the price dynamics.We then investigate the interactions among the LDAs by using the distribution of price,namely the mean-field term.A numerical method is used to solve the proposed pricing strategy.The evaluations demonstrate that the proposed pricing strategy efficiently allows the data owners from multiple LDAs to reach an equilibrium on data quantity to sell regarding the current individual price scheme.The result further demonstrates that the influential LDAs determine the final price distribution.Last but not least,it shows that cooperation among mobile data owners leads to optimal social welfare even with the additional cost of information exchange.
基金VODAN-Africathe Philips Foundation+2 种基金the Dutch Development Bank FMOCORDAIDthe GO FAIR Foundation for supporting this research
文摘The incompleteness of patient health data is a threat to the management of COVID-19 in Africa and globally.This has become particularly clear with the recent emergence of new variants of concern.The Virus Outbreak Data Network(VODAN)-Africa has studied the curation of patient health data in selected African countries and identified that health information flows often do not involve the use of health data at the point of care,which renders data production largely meaningless to those producing it.This modus operandi leads to disfranchisement over the control of health data,which is extracted to be processed elsewhere.In response to this problem,VODAN-Africa studied whether or not a design that makes local ownership and repositing of data central to the data curation process,would have a greater chance of being adopted.The design team based their work on the legal requirements of the European Union’s General Data Protection Regulation(GDPR);the FAIR Guidelines on curating data as Findable,Accessible(under well-defined conditions),Interoperable and Reusable(FAIR);and national regulations applying in the context where the data is produced.The study concluded that the visiting of data curated as machine actionable and reposited in the locale where the data is produced and renders services has great potential for access to a wider variety of data.A condition of such innovation is that the innovation team is intradisciplinary,involving stakeholders and experts from all of the places where the innovation is designed,and employs a methodology of co-creation and capacity-building.
基金VODAN-Africathe Philips Foundation+2 种基金the Dutch Development Bank FMOCORDAIDthe GO FAIR Foundation for supporting this research
文摘The field of health data management poses unique challenges in relation to data ownership, the privacy of data subjects, and the reusability of data. The FAIR Guidelines have been developed to address these challenges. The Virus Outbreak Data Network(VODAN) architecture builds on these principles, using the European Union’s General Data Protection Regulation(GDPR) framework to ensure compliance with local data regulations, while using information knowledge management concepts to further improve data provenance and interoperability. In this article we provide an overview of the terminology used in the field of FAIR data management, with a specific focus on FAIR compliant health information management, as implemented in the VODAN architecture.
