Data model fundamentals

The data model is how Nominal relates your time-series data to the units and tests it came from.

Understanding the data model

Data model core concepts defines each part of the data model and shows how time-series data relates to metadata about units, tests, and the issues flagged for review.

Applying the data model

The stories in Organize and view your data use one or more of these approaches:

• The default approach uses a time-series dataset for each of your long-lived, high-value units tested over many campaigns.

• Tagging data in a single dataset tags the data itself with metadata. This works well for production/end-of-line test because the tester only needs to know an identifier like a serial number.

• Simulations use a variant of the tagged approach which prioritizes making comparisons across executions.

Combining the default and tagged approaches covers units with datasets of their own alongside a shared source, such as a ground station, that tags its data by unit.

Complex and nested assets is an extension of the tagged approach for units built from components that are units under test themselves and can move from one unit to another.