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

Current setup (description)

So far, the data modeling activities in COVESA have been primarily centered on 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 of what requirements (i.e., functional and non-functional) are driving the design of the data model. It seems that the primary 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:

• On the one hand, there is the "conceptual area," where the controlled vocabulary has to be described and adequately documented. Data models belong to this area because they define the entities of interest in a particular domain and the possible relationships between them.
• On the other hand, we have the "application area," where a data model is used in specific implementations (e.g., databases, applications, etc.).

The figure above shows how VSS modeling belongs to the conceptual area. 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 the mechanism that 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. In this context, we mean long-term storage (e.g., a database) or short-term storage (RAM 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 shown no significant limitation until now, it becomes relevant when multiple domains are involved. Therefore, with the increasing interest in adding other domains apart from vehicle-specific data, it is crucial to define a data modeling strategy that can scale beyond tree hierarchies and vehicle-specific data.

One may argue that, with the current setup, it is also possible to describe a customised shorter spec file or use functions (e.g., overlays) to define the specification that matches the needs of the application area. Although possible, that implies creating disparate data models. Ideally, the concepts should be modelled only once in the conceptual area to serve as a controlled vocabulary. Then, an arbitrary selection of the concepts and some modifications to the constraints (e.g., min, max, etc.) can deliver the schema needed in specific use-cases, without affecting the standard definition of the concepts. Hence, the following section propose a few specific tasks to improve the data modeling workflow.

Proposal overview (description)

The idea is to define the data modelling workflow for COVESA in terms of the conceptual area (i.e., development and maintenance of the controlled vocabulary) and the application area (i.e., tools that construct a usable schema out of the data models for a particular use case).

• In the conceptual area, there should be a clear step-by-step guide on how to work with two different levels of expressiveness. This consideration is needed because a tree hierarchical model alone (what VSS has been so far) is not the best model type to handle some upcoming needs, such as data integration. Hence, the model might be selected depending on the user needs.

  • (less expressive) Tree hierarchical model, good for:

    • Information classification according to a given criteria (e.g., taxonomy, meronomy, custom tree.)

    • Naming convention following a dotted notation (i.e., concatenation of the branches)

  • (more expressive) Ontology, good for:

    • Data integration

    • Concepts re usability

    • Reasoning

    • Multiple hierarchies

    • Knowledge representation

• In the application area, the tools have to provide a mechanism to "cherry pick" (i.e., to arbitrary select) concepts of interest from one or multiple domains, including the context and pointers to the uniquely identified definition of the concepts.

In other words, the proposal is not to enforce the use of ontologies. It is rather providing a common agreement on how and when to use what data model. The idea is better explained with the following figure, which is explained below:

[1] - Generalise the current data modeling approach used in VSS to make it re usable when needed

The simplicity of VSS has proven to be a successful approach for the continuous contribution of Subject Matter Experts (SMEs); just by modifying a text file, discussing the changes, and creating a pull request. The approach itself should be generalized to serve as a guideline to describe and maintain one hierarchy. The idea here is to abstract the modelling approach used in VSS and describe it with generic terms that might be re used with other domains. This implies doing some minor adjustments to geralize the YAML-based modeling approach, and also defining a rule set to support COVESA participants to identify the need of a new tree model.

[1a] - Generic YAML-based tree modeling approach (one hierarchy)

People often refer to a hierarchical tree data model as a taxonomy. However, this is not always the case. Depending of the meaning of the implicit relationship between branches of a tree, the hierarchy can be:

To handle these semantic differences, the following tasks are proposed:

• Add a field at the beginning of the tree specification to explicitly state the tree type

  # To include at the top of the spec YAML file (the root branch)
  tree type as one of ['TAXONOMY','meronomy','CUSTOM']

• Extend the tools to interpret the tree type as follows

• Add a field in the branch definition to explicitly state the implicit relationship to the parent branch
  # Example of a branch that has a custom relationship
  PowerTrain.Charging:
    type: branch
    description: Properties related to battery charging.
    relationToParentBranch: 'functionOfVehicleComponent'

[1b] - Rule-set for a new tree model

Having an stablished approach to model a tree hierarchy does not mean that COVESA should motivate the arbitrary creation of multiple trees. Data modeling is a continuous design task that requires several iterations and a huge maintenance effort. Therefore, it is essential to define a simple set of rules that are to be satisfied before starting a new data tree that is to be developed and maintained by COVESA. Such a rule set can include, for example:

• There must exist the proven need for at least 3 branches.
• Each branch must have at least 2 leaves.
• The implicit relationship between consecutive branches must be documented.
• There must be no cross references to other existing data models.
• Branch names must use this XYZ format
• There should be at least 2 people responsible for the organization and maintenance of the model
• etc.

[2] - Extend the tools to use standard data exchange format and to allow the construction of custom schemas

Tools should be extended to use standards for the specification itself, and also to be able to arbitrarily construct the desired schema.

[2a] - A standard data exchange format

Current VSS-tools allow parsing the specification into multiple formats. The specification itself is done in the custom YAML file (i.e., with the vspec format). This is not an standard data exchange format. It is a good practice to specify taxonomies (and similar hierarchies) using standard formats [R1]. Two important reasons for that are interoperability, and re use of existing tools for editing and visualising the data models.

Here, the most prominent standards that are preferred today for these taxonomy-like data models are the Resource Description Framework (RDF), and the Simple Knowledge Organization System (SKOS).

[2b] - Allow the construction of custom schemas

The idea here is that a data model can contain much more concepts that the application area needs. This is because the application area might consist of multiple use cases. In principle, the idea here is to keep to the number of data models to maintain by COVESA to the minimum. Hence, a mechanism to construct custom schemas will decouple the need for the modification of the model.

[3] - Publish the COVESA tree(s) with standards and future-proof name spaces

Right now, the specification has a practical use after applying the vss-tools. The main identifier of a concept of interest (i.e., a tree leaf) is either the path defined with a dotted notation, or the UUID introduced recently. None of them is fully solving the identification of the resource.

The paths require a custom script that interpret them in other to do the mapping, whereas the current UUID approach does not guarantee that the identifier will remain unchanged when the path changes (e.g., a branch name change will lead to a different UUID). 

The idea here is to take advantage of the principles of the RDF, which is the identification of resource in the web. For that, one must define a so called name space that will act as the address where the model is stored (hopefully forever). For example:

@prefix vss: http://covesa.global/datamodels/vss# .

[4] - Use a COVESA tree to automatically feed one hierarchy of a domain ontology

This is the entry point for ontologies. The main limitation of a tree is that it can only handle one hierarchy. In the real world, however, the knowledge looks more like a semantic network than a simple tree.

[4a] - Domain ontology

We can take the advantage of a well-maintained tree to further enrich the vehicle data model. For example, a domain ontology can be useful for:

• Say something about the branches of the tree, and how they relate to other domains
  • Production information of a particular vehicle component
    • Material
    • Place
    • Manufacture data
• Connect vehicle data to other areas
  • Smart city
  • Traffic management
• Connect the VSS high-level view to the low-level data of the vehicle electronic system.
• etc.

[4b] - COVESA core ontology

Once multiple models are involved, we must keep them inter operable. That means that we need to also have a controlled vocabulary to categorise new concept of interest into abstract and generic categories. For example

• A ChargingSession is an Event
  • Then the concept of an Event is modelled in the COVESA core ontology
• The same applies for other common concepts like time, colours, etc.

The systematic use of these controlled vocabularies will lead to an easy data integration.

Use a methodology suitable to design a solution

Designing an artifact that solves a problem can lead to multiple valid solutions. Therefore, a simple and clear methodology to guarantee the validity of the proposed solution must contain at least these 4 elements (adapted from [R3]):

• [R1] H. Hedden, The accidental taxonomist, Third edition. Medford, New Jersey: Information Today, Inc, 2022.
• [R2] P. Alexopoulos, Semantic modeling for data: avoiding pitfalls and breaking dilemmas. O’Reilly Media, Inc., 2020.
• [R3] R. J. Wieringa, Design Science Methodology for Information Systems and Software Engineering. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. doi: 10.1007/978-3-662-43839-8.