To be able to perform an efficient early warning process for noticeable populations in the field, we need to have a semantic model to define them. This should be done via the definition of logical filter criterias based on the aspects within the existing semantic models of the use case quality (CX-0123).
To describe the popluation inside the semantic model, we want to use logical operators which also can be connected with eachother (and, or (exclusive), not).
Possible operators: Equals to, less than, grater than, not equal to, less than or equal to, grater than or equal to, starts with, ends with, contains
Relevant Standards
Example Data
productionDate[20.01.2023<x<13.06.2023] and engineSeries[EA189] and softwareCategory[TZGH12345] = true and colorID[LY7W] = false
MS1 Criteria
[x] The proposed aspect model does not exist already
[x] The proposed aspect model does not extend an existing aspect but introduces completely new functionality
[x] The proposal references a Catena-X use case
[x] Relevant standards are mentioned/linked
[x] A first draft of the data structure is provided in an example
JulianSchererZF
commented
4 months ago
Model Description
To be able to perform an efficient early warning process for noticeable populations in the field, we need to have a semantic model to define them. This should be done via the definition of logical filter criterias based on the aspects within the existing semantic models of the use case quality (CX-0123).
To describe the popluation inside the semantic model, we want to use logical operators which also can be connected with eachother (and, or (exclusive), not).
Possible operators: Equals to, less than, grater than, not equal to, less than or equal to, grater than or equal to, starts with, ends with, contains
Relevant Standards
Example Data
productionDate[20.01.2023<x<13.06.2023] and engineSeries[EA189] and softwareCategory[TZGH12345] = true and colorID[LY7W] = false
MS1 Criteria