EM-DAT-03 · Subject model · W2
Pipeline, run and data provenance
Transformation definition, job, run, inputs and outputs, lineage at the dataset and field level. High-volume logs remain in external storage.
Partially published; research continues
Claude: complete-frozen-study-and-audit; Grok: complete-browser-study.
Published results
Research note, in Russian: Canonical promotion remains held by the limits published with the profile.
Subject boundary and candidate types
- DataPipeline
- PipelineRun
- Transformation
- LineageAssertion
Deep research questions
- How to link lineage without false causality?
- How to record a manual correction?
- How to reproduce a specific output?
Verifiable invariants
- A run pins input versions
- Lineage has a source and a level of detail
- Absence of lineage does not mean absence of dependency
End-to-end acceptance scenario
Two runs, a manual adjustment and an unknown external input yield a partially proven chain, not fabricated completeness.
Negative case
Matching column names are treated as a proven transformation.
Approaches to compare
- W3C DCAT: dataset, distribution, catalog
- W3C PROV and Data Cube: provenance and observation
- SDMX and BI/data catalog practice: indicators, breakdowns, definitions and releases
Candidates in the live catalogue
- WM-DAT-005 · Data Pipeline · 0.3.0-research.1 · installable
Semantic fit requires boundary research; a published model does not by itself complete this card. - WM-DAT-006 · Data Lineage · 0.3.0-research.1 · installable
Semantic fit requires boundary research; a published model does not by itself complete this card.
Result requirements
Every card is executed together with the full research contract: definitions, fields and cardinalities, lifecycle, sources, data mastership, rights, the five object facets, at least eight invariants, positive and negative examples, dependencies, migration and applicability limits.