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Governance and Implementation of the Intelligent Data Fabric

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  • #134334

    Collaborative session notes:​​​​​​

    Introduction to Data Fabric and governance challenge (10 min)
    International Data Fabric Governance and cross-continental implementation updates (10+5 min each)

    DONA, GO-FAIR perspective (tbd)
    CNIC (Liu JIA)
    cs3mesh4eosc (Guido Aben)

    Discussion: Towards the Intelligent Data Fabric (35 min)

    Additional links to informative material
    Group home page:
    Future trends for the Data Fabric:
    Webinar: Data Fabrics
    Recommendations on virtual layering:

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    Avoid conflict with the following group (1)
    PID IG

    Avoid conflict with the following group (3)
    Persistent Identification of Instruments WG

    Group chair serving as contact person
    Tobias Weigel

    Meeting objectives
    The meeting will focus on Data Fabric governance and implementation, but also introduce new innovative areas in terms of automating the Data Fabric with Machine Learning / Artificial Intelligence.
    1) International Data Fabric Governance
    The Data Fabric is built by infrastructures and applications that rely on a diverse set of components. The interplay between components is described via recommended interfaces and protocols at the technical level. However, to fully realize the Data Fabric vision, governance must also be addressed as part of implementation. Focus topics at the heart of data fabric governance and cross-disciplinary implementation for the session include:

    Governance of different registries and other components and determining the need for overarching governance framework(s)
    What is the win-win governance model for cross-continental data fabric/infrastructure cooperation?
    What is a good mechanism to govern the institutional level, national level and international level data fabric/infrastructure?
    How does data fabric governance relate to practical implementation, e.g. at cloud storage level?

    2) Towards the Intelligent Data Fabric
    Further automating the data-centric processes in the labs is a key concern in the Data Fabric scope. Machine Learning (ML) and Artificial Intelligence (AI) now present the means to automate the processes on a wide scale, across disciplines and existing services or e-infrastructures. The objective of the meeting is therefore to discuss how to apply AI and ML concepts in practical implementation examples such as:

    automate metadata capture and transformation from structured and unstructured data
    support service chaining for data processing
    automated categorization of data products for metadata management, search and service discovery
    data reduction and summarization
    enhance data discovery, provide intelligent user assistance, recommender systems

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