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This page describes the proposal to restructure the data modelling activities within COVESA. First, the current state is presented to provide the necessary context. Then, the proposal is introduced.

Current

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setup: no clear distinction of conceptual and application areas

So far, the data modelling activities in COVESA have been mostly centred in the continuous development and maintenance of the Vehicle Signal Specification (VSS) and the tools that parse VSS into different formats. In the current setup, there is no clear description on what requirements (i.e., functional and non-functional) are driving the design of the data model. It seems that the main purpose of VSS is to serve as a naming convention for the properties of the vehicle. Nevertheless, there is little attention given to the separation of concerns:

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The figure above shows how VSS modelling modeling belongs to the conceptual area. In order to To use the specification described in VSS (i.e., a "vspec" file), one has to parse it into a specific format (e.g., JSON) by using the VSS - tools. The tools are indeed the mechanism that is making makes the VSS data model usable in the application area. From the practical point of view, the application area needs a specific schema that determines the structure in which the data is to be stored. By storage, in In this context, we mean either long-term storage (e.g., a database) or short-term storage (RAM memory and variables' allocation during application execution).

In the current setup, the whole data model is taken one-to-one and parsed as the schema for the application area. Then, it is up to the specific implementation to use custom mechanisms to ignore the overhead when only some concepts defined in the data model are required or used. Although this aspect has not shown any significant limitation until now, it becomes relevant when multiple domain are involved. Therefore, with the increasing interest in the addition of other domains apart from vehicle-specific data, it is important to define a data modelling strategy that can scale beyond tree hierarchies and vehicle-specific data.