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Data Expert Group
▶︎ Data Models and Ontologies
     ▷ Vehicle Signal Specification (VSS)
     ▷ Vehicle Signal Specification Ontology (VSSo) - W3C Collaboration
▶︎ Architecture and Infrastructure
     ▷ Data Architecture Terminology (including Logical Components)
▶︎ Interface Definition
     ▷ Vehicle API
     ▷ Vehicle Service Catalog (VSC)
     ▷ Vehicle Information Service Specification (VISS) - W3C Collaboration
▶︎ Best Practices
     ▷ Governance
     ▷ Privacy and Identity
     ▷ Data Model Definition
     ▷ API First
Data Expert Group Workshop 2023Q428 at Spring AMM
Data Expert Group Workshop 2023Q1

Electric Vehicle Charging Expert Group
▶︎ EV Charging Event Data Aggregation Project
▶︎ EV Optimization - Increase Travel Range for Fixed Battery
▶︎ Private Cross OEM Joint Compute for EV Charging

Android™ Automotive SIG
▶︎ Automotive AOSP App Framework Standardization

Security Team

Simulation and Tooling

Vehicle Experience and Content - Entertainment BoF
▶︎ In-vehicle Payment SIG

Commercial Vehicle BoF


Common Vehicle Interface Initiative

Cloud & Connected Services Project

SDV Telemetry Project - On Hold


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Industry Events 2023


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October 2022 All Member Meeting

April 2022 All Member Meeting

October 2021 All Member Meeting

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May 2019 All Member Meeting

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The Data Models and Ontologies Pillar shall:

  • host  the Vehicle  Data  Project(VSS)  ensuring  a  consistent  vehicle  data rule  set  & development of a vehicle data catalog
  • discuss and document a personal data model (PDM) and align with best practices of privacy and identity
  • host discussion on vehicle data concepts and related tooling

The Data Models and Ontologies Pillar may:

  • discuss interaction with other domain models Includes the following existing activities:•Vehicle Signal Specification project



Proposal nameAuthorSlides presented at COVESA AMM
Porto (April 2023)
Main idea(s)
Transforming from a vehicle centric data model to a domain agnostic information model

Defining the COVESA data modeling strategy and its associated artifacts

  • Use the right tool for the job.
    • Do not try to cover multiple domains with only one tree.
    • There are other types of data models that are more expressive than a tree.
  • Use an standard language to express cross-references (e.g., RDF).
  • Use ontologies to handle data integration and the identification of resources.
  • Provide the mechanisms to sync a tree to an ontology, and standardise the steps to follow.
  • Do you need a data model, or are you looking for a use-case specific data schema?
  • For more details, see the extended description.

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