Stream Forge Metric Provenance and Trust - mdebellis/SemanticKG-Design GitHub Wiki

Stream Forge — Metric Provenance and Trust

Overview

This story extends the Stream Forge example beyond basic ingestion and identity to address the problem of trust in derived metrics. It focuses on how engagement metrics are produced, transformed, and consumed, and why understanding their provenance is essential for governance and decision-making.

At this stage in the lifecycle, Stream Forge has already established stable identities for core entities such as videos and creators. The challenge now is no longer what a video is, but what claims are being made about it.

Narrative

After ingesting content metadata from multiple platforms, Stream Forge begins to receive engagement metrics such as view counts, likes, and shares. These metrics arrive through different pipelines and at different times. Some are reported directly by source platforms, while others are computed internally through aggregation or transformation.

Over time, analysts and downstream systems observe discrepancies. The same video appears with different engagement values depending on the report or dashboard being viewed. In some cases, metrics have been recomputed using updated logic; in others, source systems have retroactively corrected earlier values.

Stakeholders begin to ask not only what the numbers are, but where they came from, how they were produced, and whether they can be trusted.

Core Questions

This story drives a new class of questions:

  • Was this metric asserted by a source system or computed internally?
  • What transformation or aggregation logic produced this value?
  • Which version of the pipeline or model was used?
  • When was the metric generated, and over what input data?
  • Which values should be preferred when multiple metrics conflict?

Answering these questions requires more than storing numbers; it requires explicit modeling of lineage and process.

Requirements Implied by the Story

From this narrative, additional requirements emerge:

  • Metrics must be distinguishable from raw observations
  • Derived values must retain links to their inputs
  • Transformations must be modeled as first-class activities
  • Conflicting values must be representable without data loss
  • Consumers must be able to trace metrics back to their origin

These requirements rule out opaque aggregation pipelines that discard intermediate context.

Modeling Implications

This story motivates several important modeling decisions:

  • Metrics are modeled as entities with identity, not as anonymous literals
  • Activities that generate or transform metrics are explicitly represented
  • Inputs and outputs of transformations are linked and queryable
  • Agents responsible for producing metrics are identifiable
  • Temporal aspects of metric generation are recorded

Provenance is no longer optional metadata; it becomes a core part of the domain model.

Governance and Trust Considerations

Explicit provenance enables governance practices that are otherwise difficult or impossible:

  • Auditing how a value was produced
  • Explaining discrepancies between reports
  • Supporting regulatory or compliance review
  • Making informed decisions about data quality and trustworthiness

Rather than enforcing a single “correct” value, the model supports informed comparison and choice.

Relationship to Other Stories

This story builds directly on the identity foundations established in Story 1. Without stable entity identity, tracing metric provenance would be unreliable or meaningless.

It also prepares the ground for Story 3, where metrics and classifications are consumed by AI systems. In that context, the ability to explain and qualify answers depends critically on understanding where underlying values originate.

Related Materials

This story is supported by:

  • Competency questions related to metric lineage and trust
  • Provenance patterns used in the Stream Forge example
  • Example transformation activities and derived metrics
  • Sample queries illustrating lineage tracing

These materials make the abstract concerns raised here concrete without prescribing implementation details.