EU H2020 MSCA RISE Project FIRST - “virtual Factories: Interoperation suppoRting buSiness innovation”

FIRST – “virtual Factories: Interoperation suppoRting buSiness innovation”, is a European H2020 project, founded by the RESEARCH AND INNOVATION STAFF EXCHANGE (RISE) Work Programme as part of the Marie Skłodowska-Curie actions. The project concerns with Manufacturing 2.0 and aims at providing the new technology and methodology to describe manufacturing assets; to compose and integrate the existing services into collaborative virtual manufacturing processes; and to deal with evolution of changes. This Chapter provides an overview of the state of the art for the research topics related to the project research objectives, and then it presents the progresses the project achieved up to now towards the implementation of the proposed innovations

ODIN: A dataspace management system

ODIN is a system that supports the incremental pay-as-you-go integration of data sources into dataspaces and provides user-friendly querying mechanisms on top of them. We describe its main characteristics and underlying assumptions, including the user interactions required. Odin’s novelty lies in a largely automated bottom-up approach (i.e., driven by the sources at hand) that includes the user in the loop for disambiguation purposes. The on-site demonstration will feature an ongoing project with the World Health Organization (WHO). Online demo and videos: www.essi.upc.edu/dtim/odin/Peer ReviewedPostprint (published version

Quarry: A user-centered big data integration platform

Obtaining valuable insights and actionable knowledge from data requires cross-analysis of domain data typically coming from various sources. Doing so, inevitably imposes burdensome processes of unifying different data formats, discovering integration paths, and all this given specific analytical needs of a data analyst. Along with large volumes of data, the variety of formats, data models, and semantics drastically contribute to the complexity of such processes. Although there have been many attempts to automate various processes along the Big Data pipeline, no unified platforms accessible by users without technical skills (like statisticians or business analysts) have been proposed. In this paper, we present a Big Data integration platform (Quarry) that uses hypergraph-based metadata to facilitate (and largely automate) the integration of domain data coming from a variety of sources, and provides an intuitive interface to assist end users both in: (1) data exploration with the goal of discovering potentially relevant analysis facets, and (2) consolidation and deployment of data flows which integrate the data, and prepare them for further analysis (descriptive or predictive), visualization, and/or publishing. We validate Quarry’s functionalities with the use case of World Health Organization (WHO) epidemiologists and data analysts in their fight against Neglected Tropical Diseases (NTDs).This work is partially supported by GENESIS project, funded by the Spanish Ministerio de Ciencia, Innovación y Universidades under project TIN2016-79269-R.Peer ReviewedPostprint (author's final draft

D3M: automated data-driven decision making

Data has an undoubtedly impact on society. Storing, processing and analyzing large amounts of available data is currently one of the key success factors for an organization. Nonetheless, we are recently witnessing a change represented by huge and heterogeneous amounts of data. Thus, in order to carry on these data exploitation tasks, organizations must first perform data integration combining data from multiple sources to yield a unified view over them. In this paper, we report on the Automated Data-Driven Decision Making (D3M) project, whose main objective is to provide a mature software solution for automatic data integration with advanced decision making capabilities.This paper has been funded by the Spanish Agencia Estatal de Investigación (AEI) under project / funding scheme PDC2021-121195-I00.Peer ReviewedPostprint (published version

Planning for the Lifecycle Management and Long-Term Preservation of Research Data: A Federated Approach

Outcomes of the grant are archived here.The “data deluge” is a recent but increasingly well-understood phenomenon of scientific and social inquiry. Large-scale research instruments extend our observational power by many orders of magnitude but at the same time generate massive amounts of data. Researchers work feverishly to document and preserve changing or disappearing habitats, cultures, languages, and artifacts resulting in volumes of media in various formats. New software tools mine a growing universe of historical and modern texts and connect the dots in our semantic environment. Libraries, archives, and museums undertake digitization programs creating broad access to unique cultural heritage resources for research. Global-scale research collaborations with hundreds or thousands of participants, drive the creation of massive amounts of data, most of which cannot be recreated if lost. The University of Kansas (KU) Libraries in collaboration with two partners, the Greater Western Library Alliance (GWLA) and the Great Plains Network (GPN), received an IMLS National Leadership Grant designed to leverage collective strengths and create a proposal for a scalable and federated approach to the lifecycle management of research data based on the needs of GPN and GWLA member institutions.Institute for Museum and Library Services LG-51-12-0695-1

BIG DATA AND ANALYTICS AS A NEW FRONTIER OF ENTERPRISE DATA MANAGEMENT

Big Data and Analytics (BDA) promises significant value generation opportunities across industries. Even though companies increase their investments, their BDA initiatives fall short of expectations and they struggle to guarantee a return on investments. In order to create business value from BDA, companies must build and extend their data-related capabilities. While BDA literature has emphasized the capabilities needed to analyze the increasing volumes of data from heterogeneous sources, EDM researchers have suggested organizational capabilities to improve data quality. However, to date, little is known how companies actually orchestrate the allocated resources, especially regarding the quality and use of data to create value from BDA. Considering these gaps, this thesis – through five interrelated essays – investigates how companies adapt their EDM capabilities to create additional business value from BDA. The first essay lays the foundation of the thesis by investigating how companies extend their Business Intelligence and Analytics (BI&A) capabilities to build more comprehensive enterprise analytics platforms. The second and third essays contribute to fundamental reflections on how organizations are changing and designing data governance in the context of BDA. The fourth and fifth essays look at how companies provide high quality data to an increasing number of users with innovative EDM tools, that are, machine learning (ML) and enterprise data catalogs (EDC). The thesis outcomes show that BDA has profound implications on EDM practices. In the past, operational data processing and analytical data processing were two “worlds” that were managed separately from each other. With BDA, these "worlds" are becoming increasingly interdependent and organizations must manage the lifecycles of data and analytics products in close coordination. Also, with BDA, data have become the long-expected, strategically relevant resource. As such data must now be viewed as a distinct value driver separate from IT as it requires specific mechanisms to foster value creation from BDA. BDA thus extends data governance goals: in addition to data quality and regulatory compliance, governance should facilitate data use by broadening data availability and enabling data monetization. Accordingly, companies establish comprehensive data governance designs including structural, procedural, and relational mechanisms to enable a broad network of employees to work with data. Existing EDM practices therefore need to be rethought to meet the emerging BDA requirements. While ML is a promising solution to improve data quality in a scalable and adaptable way, EDCs help companies democratize data to a broader range of employees

Data Spaces

This open access book aims to educate data space designers to understand what is required to create a successful data space. It explores cutting-edge theory, technologies, methodologies, and best practices for data spaces for both industrial and personal data and provides the reader with a basis for understanding the design, deployment, and future directions of data spaces. The book captures the early lessons and experience in creating data spaces. It arranges these contributions into three parts covering design, deployment, and future directions respectively. The first part explores the design space of data spaces. The single chapters detail the organisational design for data spaces, data platforms, data governance federated learning, personal data sharing, data marketplaces, and hybrid artificial intelligence for data spaces. The second part describes the use of data spaces within real-world deployments. Its chapters are co-authored with industry experts and include case studies of data spaces in sectors including industry 4.0, food safety, FinTech, health care, and energy. The third and final part details future directions for data spaces, including challenges and opportunities for common European data spaces and privacy-preserving techniques for trustworthy data sharing. The book is of interest to two primary audiences: first, researchers interested in data management and data sharing, and second, practitioners and industry experts engaged in data-driven systems where the sharing and exchange of data within an ecosystem are critical

Data Spaces

This open access book aims to educate data space designers to understand what is required to create a successful data space. It explores cutting-edge theory, technologies, methodologies, and best practices for data spaces for both industrial and personal data and provides the reader with a basis for understanding the design, deployment, and future directions of data spaces. The book captures the early lessons and experience in creating data spaces. It arranges these contributions into three parts covering design, deployment, and future directions respectively. The first part explores the design space of data spaces. The single chapters detail the organisational design for data spaces, data platforms, data governance federated learning, personal data sharing, data marketplaces, and hybrid artificial intelligence for data spaces. The second part describes the use of data spaces within real-world deployments. Its chapters are co-authored with industry experts and include case studies of data spaces in sectors including industry 4.0, food safety, FinTech, health care, and energy. The third and final part details future directions for data spaces, including challenges and opportunities for common European data spaces and privacy-preserving techniques for trustworthy data sharing. The book is of interest to two primary audiences: first, researchers interested in data management and data sharing, and second, practitioners and industry experts engaged in data-driven systems where the sharing and exchange of data within an ecosystem are critical

Strategies of development and maintenance in supervision, control, synchronization, data acquisition and processing in light sources

Programa Oficial de Doutoramento en Tecnoloxías da Información e as Comunicacións. 5032V01[Resumo] Os aceleradores de partículas e fontes de luz sincrotrón, evolucionan constantemente para estar na vangarda da tecnoloxía, levando os límites cada vez mais lonxe para explorar novos dominios e universos. Os sistemas de control son unha parte crucial desas instalacións científicas e buscan logra-la flexibilidade de manobra para poder facer experimentos moi variados, con configuracións diferentes que engloban moitos tipos de detectores, procedementos, mostras a estudar e contornas. As propostas de experimento son cada vez máis ambiciosas e van sempre un paso por diante do establecido. Precísanse detectores cada volta máis rápidos e eficientes, con máis ancho de banda e con máis resolución. Tamén é importante a operación simultánea de varios detectores tanto escalares como mono ou bidimensionáis, con mecanismos de sincronización de precisión que integren as singularidades de cada un. Este traballo estuda as solucións existentes no campo dos sistemas de control e adquisición de datos nos aceleradores de partículas e fontes de luz e raios X, ó tempo que explora novos requisitos e retos no que respecta á sincronización e velocidade de adquisición de datos para novos experimentos, a optimización do deseño, soporte, xestión de servizos e custos de operación. Tamén se estudan diferentes solucións adaptadas a cada contorna.[Resumen] Los aceleradores de partículas y fuentes de luz sincrotrón, evolucionan constantemente para estar en la vanguardia de la tecnología, y poder explorar nuevos dominios. Los sistemas de control son una parte fundamental de esas instalaciones científicas y buscan lograr la máxima flexibilidad para poder llevar a cabo experimentos más variados, con configuraciones diferentes que engloban varios tipos de detectores, procedimientos, muestras a estudiar y entornos. Los experimentos se proponen cada vez más ambiciosos y en ocasiones más allá de los límites establecidos. Se necesitan detectores cada vez más rápidos y eficientes, con más resolución y ancho de banda, que puedan sincronizarse simultáneamente con otros detectores tanto escalares como mono y bidimensionales, integrando las singularidades de cada uno y homogeneizando la adquisición de datos. Este trabajo estudia los sistemas de control y adquisición de datos de aceleradores de partículas y fuentes de luz y rayos X, y explora nuevos requisitos y retos en lo que respecta a la sincronización y velocidad de adquisición de datos, optimización y costo-eficiencia en el diseño, operación soporte, mantenimiento y gestión de servicios. También se estudian diferentes soluciones adaptadas a cada entorno.[Abstract] Particle accelerators and photon sources are constantly evolving, attaining the cutting-edge technologies to push the limits forward and explore new domains. The control systems are a crucial part of these installations and are required to provide flexible solutions to the new challenging experiments, with different kinds of detectors, setups, sample environments and procedures. Experiment proposals are more and more ambitious at each call and go often a step beyond the capabilities of the instrumentation. Detectors shall be faster, with higher efficiency, more resolution, more bandwidth and able to synchronize with other detectors of all kinds; scalars, one or two-dimensional, taking into account their singularities and homogenizing the data acquisition. This work examines the control and data acquisition systems for particle accelerators and X- ray / light sources and explores new requirements and challenges regarding synchronization and data acquisition bandwidth, optimization and cost-efficiency in the design / operation / support. It also studies different solutions depending on the environment

Designing Data Spaces

This open access book provides a comprehensive view on data ecosystems and platform economics from methodical and technological foundations up to reports from practical implementations and applications in various industries. To this end, the book is structured in four parts: Part I “Foundations and Contexts” provides a general overview about building, running, and governing data spaces and an introduction to the IDS and GAIA-X projects. Part II “Data Space Technologies” subsequently details various implementation aspects of IDS and GAIA-X, including eg data usage control, the usage of blockchain technologies, or semantic data integration and interoperability. Next, Part III describes various “Use Cases and Data Ecosystems” from various application areas such as agriculture, healthcare, industry, energy, and mobility. Part IV eventually offers an overview of several “Solutions and Applications”, eg including products and experiences from companies like Google, SAP, Huawei, T-Systems, Innopay and many more. Overall, the book provides professionals in industry with an encompassing overview of the technological and economic aspects of data spaces, based on the International Data Spaces and Gaia-X initiatives. It presents implementations and business cases and gives an outlook to future developments. In doing so, it aims at proliferating the vision of a social data market economy based on data spaces which embrace trust and data sovereignty