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IDS-RAM 4
IDS-RAM 4
  • README
  • Front Matter
    • Front Matter
    • Contributing Projects
  • Introduction
    • 1. Introduction
      • 1.1 Goals of the International Data Spaces
      • 1.2 Purpose and Structure of the Reference Architecture
      • 1.3 Relation to other IDSA assets
  • Context of the International Data Spaces
    • 2. Context of the International Data Spaces
      • 2.1 Data-Driven Business Ecosystems
      • 2.2 Data Sovereignty as a Key Capability
      • 2.3 Data as an Economic Good
      • 2.4 Data Exchange and Data Sharing
      • 2.5 Meaningful data
      • 2.6 Industrial Cloud Platforms
      • 2.7 Big Data and Artificial Intelligence
      • 2.8 The Internet of Things and the Industrial Internet of Things
      • 2.9 Blockchain
      • 2.10 Federated frameworks for data sharing agreements and terms of use
      • 2.11 General Data Protection Regulation
      • 2.12 Contribution of the International Data Spaces to Industry 4.0 and the Data Economy
      • 2.13 Privacy in the connected world
  • Layers of the Reference Architecture Model
    • 3 Layers of the Reference Architecture Model
      • 3.1 Business Layer
        • 3.1.1 Roles in the International Data Spaces
        • 3.1.2 Interaction of Roles
        • 3.1.3 Digital Identities
        • 3.1.4 Usage Contracts
      • 3.2 Functional Layer
      • 3.3 Information Layer
      • 3.4 Process Layer
        • 3.4.1 Onboarding
        • 3.4.2 Data Offering
        • 3.4.3 Contract Negotiation
        • 3.4.4 Exchanging Data
        • 3.4.5 Publishing and using Data Apps
        • 3.4.6 Policy Enforcement
      • 3.5 System Layer
        • 3.5.1 Identity Provider
        • 3.5.2 IDS Connector
        • 3.5.3 App Store and App Ecosystem
        • 3.5.4 Metadata Broker
        • 3.5.5 Clearing House
        • 3.5.6 Vocabulary Hub
  • Perspectives of the Reference Architecture Model
    • 4 Perspectives of the Reference Architecture Model
      • 4.1 Security Perspective
        • 4.1.1 Security Aspects addressed by the different Layers
        • 4.1.2 Identity and Trust Management
        • 4.1.3 Securing the Platform
        • 4.1.4 Securing Applications
        • 4.1.5 Securing Interactions between IDS components
        • 4.1.6 Usage Control
      • 4.2 Certification Perspective
        • 4.2.1 Certification Aspects Addressed by the Different Layers of the IDS-RAM
        • 4.2.2 Roles
        • 4.2.3 Operational Environment Certification
        • 4.2.4 Component Certification
        • 4.2.5 Processes
      • 4.3 Data Governance Perspective
        • 4.3.1 Governance Aspects Addressed by the Different Layers of the IDS-RAM
        • 4.3.2 Data Governance Model
        • 4.3.3 Data as an Economic Good
        • 4.3.4 Data Ownership
        • 4.3.5 Data Sovereignty
        • 4.3.6 Data Quality
        • 4.3.7 Data Provenance
        • 4.3.8 Data Space Instances
        • 4.3.9 IDS Rulebook
        • 4.3.10 Privacy Perspective
        • 4.3.11 Governance for Vocabularies
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  1. Context of the International Data Spaces
  2. 2. Context of the International Data Spaces

2.6 Industrial Cloud Platforms

The growing number of industrial cloud platforms will also drive the need for a standard for data sovereignty. With a lot of different platforms emerging -- driven by technology providers, software companies, system integrators, but also existing intermediaries -- it is very likely that the platform landscape will be very heterogeneous, at least for some time. Platform providers will increasingly have to provide capabilities for secure and trusted data exchange and data sharing between their own platform and other platforms in the ecosystem.

Furthermore, the cloud platform landscape is likely to be characterized by a plurality of architectural patterns, ranging from approaches characterized by a high level of centralization (e.g. data lakes) to concepts promoting utmost decentralization (e.g. distributed applications using blockchain technology).

Which platform a data owner or data provider may choose will depend on the business criticality and the economic value of the data goods they want to exchange and share. As the data resource of a company consists of data of different criticality and value, it can be expected that many companies will use different platforms for different purposes.

To make use of cloud platforms in industrial cases and to maintain data sovereignty, several preconditions must be fulfilled (especially in use cases in which different data providers bring together data in one cloud environment for being analyzed or used). The sovereign decision on a certain cloud environment must be enabled by interoperability from the perspective of data and from the perspective of the underlying software (i.e., portability of a service into different cloud environments). In addition, the capabilities of the service and the runtime must be transparent and verifiable to establish trust in the cloud environment to be used. To achieve and maintain interoperability and trust among the parties connected via a cloud environment, a governance model must be established and enforced with regard to technology, operational and administrative processes, and legal agreements.

Last updated 2 years ago