Interoperable Way Research Articles

Genie: the first open-source ISO/IEC encoder for genomic data

For the last two decades, the amount of genomic data produced by scientific and medical applications has been growing at a rapid pace. To enable software solutions that analyze, process, and transmit these data in an efficient and interoperable way, ISO and IEC released the first version of the compression standard MPEG-G in 2019. However, non-proprietary implementations of the standard are not openly available so far, limiting fair scientific assessment of the standard and, therefore, hindering its broad adoption. In this paper, we present Genie, to the best of our knowledge the first open-source encoder that compresses genomic data according to the MPEG-G standard. We demonstrate that Genie reaches state-of-the-art compression ratios while offering interoperability with any other standard-compliant decoder independent from its manufacturer. Finally, the ISO/IEC ecosystem ensures the long-term sustainability and decodability of the compressed data through the ISO/IEC-supported reference decoder.

Shared Digital Product Passport for holistic lifecycle assessment

On top of geopolitical tensions and resulting supply chain disruptions, companies are increasingly challenged to meet policy standards for the circular economy. One promising approach is extending the principles of Shared Production to the product lifecycle. This allows the implementation of a Digital Product Passport using Gaia-X principles in combination with the Asset Administration Shell. Distributed hosted submodels allow a service provider to access specific product data in an interoperable way. This approach is placed in the context of current research and demonstrated using a practical example of Scope 3 carbon footprint calculation.

Connecting Producers and Recyclers: A Digital Product Passport Concept and Implementation Suitable for End-of-Life Management

To facilitate the shift from a Linear to a Circular Economy, it is essential to alter the manner in which we manufacture, use, and dispose of products. Interoperable information exchange among various stakeholders throughout the product lifecycle is crucial if we wish to encourage reuse, repair, remanufacturing, refurbishing, or recycling. The Digital Product Passport (DPP) is a concept for capturing product information. This paper presents an end-to-end process for sharing product-related information along the product lifecycle explicitly designed to cater to the needs of all stakeholders, including end-of-life actors. This process is based on a DPP concept and implementation called the Digital Lifecycle Passport and a multi-stakeholder platform. Technically, it relies on the Asset Administration Shell (AAS), which enables information to be shared in a structured way. We demonstrate how this process can improve waste sorting and how it can be used for life cycle assessment (LCA). LCA results are captured in an interoperable way based on DIN SPEC 91474, which will facilitate automatic documentation for regulatory purposes in the future. Our approach is a forerunner in the sense that it uses the DPP and the AAS for end-of-life management.

The usage of data in NHS primary care commissioning: a realist evaluation

BackgroundTo improve health outcomes and address mounting costs pressures, policy-makers have encouraged primary care commissioners in the British National Health Service (NHS) to increase the usage of data in decision-making. However, there exists limited research on this topic. In this study, we aimed to understand how and why primary care commissioners use data (i.e. quantitative, statistical information) to inform commissioning, and what outcomes this leads to.MethodsA realist evaluation was completed to create context-mechanism-outcome configurations (CMOs) relating to the contexts influencing the usage of data in primary care commissioning. Using a realist logic of analysis and drawing on substantive theories, we analysed qualitative content from 30 interviews and 51 meetings (51 recordings and 19 accompanying meeting minutes) to develop CMOs. Purposive sampling was used to recruit interviewees from diverse backgrounds.ResultsThirty-five CMOs were formed, resulting in an overarching realist programme theory. Thirteen CMOs were identical and 3 were truncated versions of those formed in an existing realist synthesis on the same topic. Seven entirely new CMOs, and 12 refined and enhanced CMOs vis-à-vis the synthesis were created. The findings included CMOs containing contexts which facilitated the usage of data, including the presence of a data champion and commissioners’ perceptions that external providers offered new skillsets and types of data. Other CMOs included contexts presenting barriers to using data, such as data not being presented in an interoperable way with consistent definitions, or financial pressures inhibiting commissioners’ abilities to make evidence-based decisions.ConclusionsCommissioners are enthusiastic about using data as a source of information, a tool to stimulate improvements, and a warrant for decision-making. However, they also face considerable challenges when using them. There are replicable contexts available to facilitate commissioners’ usage of data, which we used to inform policy recommendations. The findings of this study and our recommendations are pertinent in light of governments’ increasing commitment to data-driven commissioning and health policy-making.

Federated causal inference based on real-world observational data sources: application to a SARS-CoV-2 vaccine effectiveness assessment

IntroductionCausal inference helps researchers and policy-makers to evaluate public health interventions. When comparing interventions or public health programs by leveraging observational sensitive individual-level data from populations crossing jurisdictional borders, a federated approach (as opposed to a pooling data approach) can be used. Approaching causal inference by re-using routinely collected observational data across different regions in a federated manner, is challenging and guidance is currently lacking. With the aim of filling this gap and allowing a rapid response in the case of a next pandemic, a methodological framework to develop studies attempting causal inference using federated cross-national sensitive observational data, is described and showcased within the European BeYond-COVID project.MethodsA framework for approaching federated causal inference by re-using routinely collected observational data across different regions, based on principles of legal, organizational, semantic and technical interoperability, is proposed. The framework includes step-by-step guidance, from defining a research question, to establishing a causal model, identifying and specifying data requirements in a common data model, generating synthetic data, and developing an interoperable and reproducible analytical pipeline for distributed deployment. The conceptual and instrumental phase of the framework was demonstrated and an analytical pipeline implementing federated causal inference was prototyped using open-source software in preparation for the assessment of real-world effectiveness of SARS-CoV-2 primary vaccination in preventing infection in populations spanning different countries, integrating a data quality assessment, imputation of missing values, matching of exposed to unexposed individuals based on confounders identified in the causal model and a survival analysis within the matched population.ResultsThe conceptual and instrumental phase of the proposed methodological framework was successfully demonstrated within the BY-COVID project. Different Findable, Accessible, Interoperable and Reusable (FAIR) research objects were produced, such as a study protocol, a data management plan, a common data model, a synthetic dataset and an interoperable analytical pipeline.ConclusionsThe framework provides a systematic approach to address federated cross-national policy-relevant causal research questions based on sensitive population, health and care data in a privacy-preserving and interoperable way. The methodology and derived research objects can be re-used and contribute to pandemic preparedness.

PerfSim: A Performance Simulator for Cloud Native Microservice Chains

Cloud native computing paradigm allows microservice-based applications to take advantage of cloud infrastructure in a scalable, reusable, and interoperable way. However, in a cloud native system, the vast number of configuration parameters and highly granular resource allocation policies can significantly impact the performance and deployment cost. For understanding and analyzing these implications in an easy, quick, and cost-effective way, we present PerfSim, a discrete-event simulator for approximating and predicting the performance of cloud native service chains in user-defined scenarios. To this end, we proposed a systematic approach for modeling the performance of microservices endpoint functions by collecting and analyzing their performance and network traces. With a combination of the extracted models and user-defined scenarios, PerfSim can then simulate the performance behavior of all services over a given period and provide an approximation for system KPIs, such as requests' average response time. Using the processing power of a single laptop, we evaluated both simulation accuracy and speed of PerfSim in 104 prevalent scenarios and compared the simulation results with the identical deployment in a real Kubernetes cluster. We achieved ~81-99% simulation accuracy in approximating the average response time of incoming requests and ~16-1200 times speed-up factor for the simulation.

Fuzzy Controllers Instead of Classical PIDs in HVAC Equipment: Dusting Off a Well-Known Technology and Today’s Implementation for Better Energy Efficiency and User Comfort

Cutting-edge building energy management systems (BEMS) interact with heating, ventilation, air conditioning (HVAC) systems, which generally account for much of the energy consumption. Major attention is focused on the BEMS themselves, barring on-field equipment. In HVAC equipment, sub-optimal controller settings may lead to energy losses and user discomfort, for instance, due to oscillations of air temperature and fan speeds. The way to solve this problem could be to replace classical PID controllers with an alternative concept that does not require tuning and works optimally for a wide range of parameters. This paper compares a fuzzy logic controller (FLC) with a standard PID for a model-based simulation of an HVAC system in Simulink for different conditions using real building measurement data. The end result is the implementation of the developed methods in a newly designed universal control board for air handling units (AHU). The proposed FLC achieves better integral control quality indicators (IAE, ISE, ITAE, ITSE) by at least 27.4%, and smaller supply air temperature variation; the daily mean square error (MSE) was reduced by an average of 36%, which leads immediately to better occupant comfort and a presumed reduction in energy consumption. Compared to the untuned PID, energy consumption was 12.7% lower; this will ensure improved economy from the lowest level, and paves the way for interoperability with high-level energy management schemes.

FAIRification of health-related data using semantic web technologies in the Swiss Personalized Health Network

The Swiss Personalized Health Network (SPHN) is a government-funded initiative developing federated infrastructures for a responsible and efficient secondary use of health data for research purposes in compliance with the FAIR principles (Findable, Accessible, Interoperable and Reusable). We built a common standard infrastructure with a fit-for-purpose strategy to bring together health-related data and ease the work of both data providers to supply data in a standard manner and researchers by enhancing the quality of the collected data. As a result, the SPHN Resource Description Framework (RDF) schema was implemented together with a data ecosystem that encompasses data integration, validation tools, analysis helpers, training and documentation for representing health metadata and data in a consistent manner and reaching nationwide data interoperability goals. Data providers can now efficiently deliver several types of health data in a standardised and interoperable way while a high degree of flexibility is granted for the various demands of individual research projects. Researchers in Switzerland have access to FAIR health data for further use in RDF triplestores.

Reuse of e-learning personalization components

Personalized learning systems use several components in order to create courses adapted to the learners’characteristics. Current emphasis on the reduction of costs of development of new resources has motivated the reuse of the e-learning personalization components in the creation of new components. Several systems have been proposed in the literature. Each system implements a specific approach and includes a set of software components. However, many of these components are not easily reusable. This paper proposes an architecture, which aims to improve the representation of learning components in a reusable and interoperable way. As a result, these components could be integrated easily for the creation of personalized learning courses. This architecture consists of five main packages: learner model, adaptation model, reuse facilities, learner interface and pedagogical knowledge. An experiment is conducted to validate the proposed architecture. The obtained results illustrate the optimal composition of e-learning personalization components through an example.

4.Q. Workshop: Showcasing PHIRI use case results measuring the impact of COVID-19 on population health

Abstract The COVID-19 pandemic has clearly demonstrated the urgent need for a cross-border and structured European mechanism to exchange, organise and access reliable health information between countries, especially in the area of population health. Population health information, defined by data on health status, health determinants and healthcare systems performance, allows for oriented research to increase the knowledge base in Europe and underpin political decision-making. Its exchange requires timely and topical provision of high-quality health information. There are many indirect effects of the COVID-19 pandemic that affect health through various pathways including secondary consequences on health and wellbeing due to delayed prevention, diagnosis and medical treatment. Within the Population Health Information Research Infrastructure (PHIRI) we look at four use cases measuring the impact of COVID-19 on population health and demonstrating how a broad variety of routine data can be pooled and/or used for secondary analysis in a distributed way across Europe aiming to facilitate research by making scalable, reproducible methods available. These use cases represent pilot activities for the benefits and added value of an infrastructure supporting federated analysis by bringing together data from different European countries and feeding the results into the federated research infrastructure. In over 20 data hubs, data is mobilised and ready to be analysed in a distributed manner. The use case outputs will be processed in an interoperable way by formalising data models, data management processes and analytical pipelines, all of which are part of the client-server PHIRI federated infrastructure implemented as here 10.5281/zenodo.6483177. The workshop aims to ensure a better understanding of COVID-19 impacts in specific subgroups and risk settings by conducting research through real-life use cases of immediate relevance. The FAIRified use cases analysis results focusing on comparisons between countries are presented and provide actionable outcomes to guide policy makers in preparedness and response scenarios. Knowledge and expertise developed across Europe is shared in this workshop. The four presentations will focus on selected aspects of COVID-19 impacts on population health. The first presentation will be on direct and indirect determinants of COVID-19 infection and outcomes in vulnerable population groups with reference to inequalities. This will be followed by a contribution of COVID-19 related delayed care in breast cancer patients. The third presentation looks at the impacts of COVID-19 on perinatal health inequalities followed by the fourth on insights in COVID-19 related changes in population mental health. Exchange with the audience will facilitate knowledge and opinion exchange through an interactive Mentimeter poll during the session. Key messages • The results will support the exchange of knowledge and expertise by facilitating insights in the impacts of COVID-19 in specific subgroups and risk settings compared across European countries. • Actionable outcomes to guide political decision-making in preparedness and response scenarios will be provided.

Transforming Points of Single Contact Data into Linked Data

Open data portals contain valuable information for citizens and business. However, searching for information can prove to be tiresome even in portals tackling domains similar information. A typical case is the information residing in the European Commission’s portals supported by Member States aiming to facilitate service provision activities for EU citizens and businesses. The current work followed the FAIR principles (Findability, Accessibility, Interoperability, and Reuse of digital assets) as well as the GO-FAIR principles and tried to transform raw data into fair data. The innovative part of this work is the mapping of information residing in various governmental portals (Points of Single Contacts) by transforming information appearing in them in RDF format (i.e., as Linked data), in order to make them easily accessible, exchangeable, interoperable and publishable as linked open data. Mapping was performed using the semantic model of a single portal, i.e., the enriched Greek e-GIF ontology and by retrieving and analyzing raw, i.e., non-FAIR data, by defining the semantic model and by making data linkable. The Data mapping process proved to require a significant manual effort and revealed that data value remains unexplored due to poor data representation. It also highlighted the need for appropriately designing and implementing horizontal actions addressing an important number of recipients in an interoperable way.

GEOSPATIAL DATA EXCHANGE USING BINARY DATA SERIALIZATION APPROACHES

Abstract. In this paper we investigate the benefits of binary data serialization as a means of storing and sharing large amounts of geospatial data in an interoperable way. De-facto text-based exchange encodings typically exposed by modern Application Programming Interfaces (APIs), including eXtensible Markup Language (XML) and JavaScript Object Notation (JSON), are generally inefficient for an increasingly higher number of applications due to their inflated volumes of data, low speed and the high computational cost for parsing and processing. In this work we consider comparisons of JSON/Geospatial JSON (GeoJSON) and two popular binary data encodings (Protocol Buffers and Apache Avro) for storing and sharing geospatial data. Using a number of experiments, we illustrate the advantages and disadvantages of both approaches for common workflows that make use of geospatial data encodings such as GeoPackage and GeoJSON. The paper contributes a number of practical recommendations around the potential for binary data serialization for interoperable (geospatial) data storage and sharing in the future.

Bitcoin is so Last Decade – How Decentralized Finance (DeFi) could Shape the Digital Economy

Ever since the Global Financial Crisis, the financial system has seen an accelerated level of innovation. Bitcoin offered a decentralized alternative to money, with thousands of other cryptoassets quickly emerging. Most recently, Decentralized Finance (DeFi) promises to offer a new digital economy, with smart contracts replacing the need for financial intermediaries. This new market aims to reinvent traditional financial products in a more transparent and interoperable way. In this paper we aim to undertake an extensive literature review of the financial stability risks posed by the fast-growing DeFi market and assess these using the Financial Stability Board’s fintech framework. We find that DeFi could pose several challenges to traditional financial infrastructure, primarily by increasing interlinkages between cryptoassets and traditional financial markets. However, if DeFi evolves in a safe way, it could increase competition and financial inclusion with overall positive implications for financial stability.

Implementation of Privacy and Security for a Genomic Information System Based on Standards.

Genomic information is a very sensitive type of digital information as it not only applies to a person, but also to close relatives. Therefore, privacy provision is key to protecting genomic information from unauthorized access. It is worth noting that most of the current genomic information formats do not provide specific mechanisms by which to secure the stored information. In order to solve, among other things, the privacy provision issue, we proposed the GIPAMS (Genomic Information Protection And Management System) modular architecture, which is based on the use of standards such as ISO/IEC 23092 and a few GA4GH (Global Alliance for Genomics and Health) initiatives. Some of the GIPAMS modules have already been implemented, mainly based on ISO/IEC 23092 features, and we are conducting work on the complete version of the architecture, and other standards are also considered. One of the objectives of GIPAMS is to enable the management of different formats of genomic information in a unique and interoperable way, providing privacy and security for formats that do not currently support them.

An end-to-end big data analytics platform for IoT-enabled smart factories: A case study of battery module assembly system for electric vehicles

Within the concept of factories of the future, big data analytics systems play a critical role in supporting decision-making at various stages across enterprise processes. However, the design and deployment of industry-ready, lightweight, modular, flexible, and cost efficient big data analytics solutions remains one of the main challenges towards the Industry 4.0 enabled digital transformation. This paper presents an end-to-end IoT-based big data analytics platform that consists of five interconnected layers and several components for data acquisition, integration, storage, analytics and visualisation purposes. The platform architecture benefits from state-of-the-art technologies and integrates them in a systematic and interoperable way with clear information flows. The developed platform has been deployed in an electric vehicle battery module assembly automation system designed by the Automation Systems Group at the University of Warwick, the UK. The developed proof-of-concept solution demonstrates how a wide variety of tools and methods can be orchestrated to work together aiming to support decision-making and to improve both process and product qualities in smart manufacturing environments.

A collaborative semantic-based provenance management platform for reproducibility.

Scientific data management plays a key role in the reproducibility of scientific results. To reproduce results, not only the results but also the data and steps of scientific experiments must be made findable, accessible, interoperable, and reusable. Tracking, managing, describing, and visualizing provenance helps in the understandability, reproducibility, and reuse of experiments for the scientific community. Current systems lack a link between the data, steps, and results from the computational and non-computational processes of an experiment. Such a link, however, is vital for the reproducibility of results. We present a novel solution for the end-to-end provenance management of scientific experiments. We provide a framework, CAESAR (CollAborative Environment for Scientific Analysis with Reproducibility), which allows scientists to capture, manage, query and visualize the complete path of a scientific experiment consisting of computational and non-computational data and steps in an interoperable way. CAESAR integrates the REPRODUCE-ME provenance model, extended from existing semantic web standards, to represent the whole picture of an experiment describing the path it took from its design to its result. ProvBook, an extension for Jupyter Notebooks, is developed and integrated into CAESAR to support computational reproducibility. We have applied and evaluated our contributions to a set of scientific experiments in microscopy research projects.

Augmented reality-based border management

A proficient border management has typically been tied to its capability to support information structuring and to make exchanges from the distributed sources. The lack of a proper access interface to information at the right time and the right place to conduct various activities. Augmented reality (AR) has been proposed as an efficient interface in order to improve the efficiency and the effectiveness of activities in the real world. There has only been a limited amount of research that has evaluated the effectiveness and usability of AR in the border management domain. This research aims to evaluate the effectiveness of border management and AR system integration to enhance the activities’ efficiency through improving the information retrieval process. The system development steps were adopted to design, develop, and evaluate the border management AR (BM AR) system. The system contains three AR services that include pointing to border objects, showing the borderline, and locating border objects. The system also integrates the information from different resources in an interoperable way using GIS web services. The results revealed the effectiveness of using AR for border activities, which can reduce the operating costs and effectively, access the required information for doing different activities in the border field.

End-to-End provenance representation for the understandability and reproducibility of scientific experiments using a semantic approach

BackgroundThe advancement of science and technologies play an immense role in the way scientific experiments are being conducted. Understanding how experiments are performed and how results are derived has become significantly more complex with the recent explosive growth of heterogeneous research data and methods. Therefore, it is important that the provenance of results is tracked, described, and managed throughout the research lifecycle starting from the beginning of an experiment to its end to ensure reproducibility of results described in publications. However, there is a lack of interoperable representation of end-to-end provenance of scientific experiments that interlinks data, processing steps, and results from an experiment’s computational and non-computational processes.ResultsWe present the “REPRODUCE-ME” data model and ontology to describe the end-to-end provenance of scientific experiments by extending existing standards in the semantic web. The ontology brings together different aspects of the provenance of scientific studies by interlinking non-computational data and steps with computational data and steps to achieve understandability and reproducibility. We explain the important classes and properties of the ontology and how they are mapped to existing ontologies like PROV-O and P-Plan. The ontology is evaluated by answering competency questions over the knowledge base of scientific experiments consisting of computational and non-computational data and steps.ConclusionWe have designed and developed an interoperable way to represent the complete path of a scientific experiment consisting of computational and non-computational steps. We have applied and evaluated our approach to a set of scientific experiments in different subject domains like computational science, biological imaging, and microscopy.

Use of Ontologies in Product Information Management: A Proposal for a Multinational Engineering and Technology Company

The PIM system importance has increased due to technical sophistication of products, their need of internal management or external publishment. ERP and CCMS systems should be integrated with a PIM system that acts as the “backbone” of product information. This project proposed an ontology-centric solution to manage product information for complex modular systems available in the catalog of one of the largest multinational organizations in engineering and technology sector. Based on action-research methodology, existing taxonomies of online product catalog at ERP and CCMS systems were analyzed, an ontology of updated taxonomies was created using Protégé tool and validated by experts. This solution was anchored in some Industry 4.0 and 5.0 pillars, by formalizing smart manufacturing knowledge in an interoperable way between multiple systems and allowing a tailored customer experience, based on interactive products and hyper customization. The organization can now be more efficient in managing individual or integrated products as complex modular systems and in communicating with customers.

Towards Secure and Interoperable Underwater Acoustic Communications: Current Activities in NATO IST-174 Research Task Group

Although the area of underwater acoustic networking is getting mature and critical for a wide variety of commercial applications, the lack of secure underwater communication standards presents a major impediment for adopting these networking technologies within NATO operations. The NATO Research Task Group IST-174 “Secure Underwater Communications for Heterogeneous Network-enabled Operations” aims to study and to demonstrate secure underwater communications in a holistic approach. In particular, this group's ambition is to define waveforms, network protocols and architectures that will enable underwater connectivity in a secure and interoperable way. To that effect, this paper presents various security concepts and communication systems currently under development that could be included in those standardisation efforts.