Dataset discovery
Large objects are found by querying run metadata rather than by directory convention.
- Vector
- JSON / documents
A dataset found by a query against its metadata, not by remembering a directory.
Where it starts
Instrument and simulation output
Datasets, runs and derived products at large sizes. It is the first of 4 workloads running in Scientific Research.
The question it raises
Dataset discovery
Large objects are found by querying run metadata rather than by directory convention.
Why one question is hard
From Hypothesis to Publish
Scientific Research data moves through 4 stages — Hypothesis → Experiment → Compute → Publish. The shapes in play are Objects, JSON / documents, Time series, SQL, Vector, and answering one question means reading across all of them.
What PLOMID contributes
Research data is defined by its metadata. These are the parts that keep metadata first class.
- Objects with queryable metadata Large assets are found by a query against their metadata rather than by a naming convention.
- Documents beside rows Fields that keep changing shape stay queryable instead of being exported into a separate document store.
- Retrieval as an access path Similarity search arrives as another way into the same data rather than a second copy to keep in step.