---
title: `nexyron.dataset_materialized_drop`
url: https://nexyron.tech/docs/cypher/procedures/abstraction-platform/runtime/nexyron-dataset-materialized-drop
description: nexyron.dataset materialized drop drop one cached materialized dataset artifact
---

# `nexyron.dataset_materialized_drop`

## Generic Description

`nexyron.dataset_materialized_drop` drop one cached materialized dataset artifact

```cypher
CALL nexyron.procedures()
YIELD name, description, parameters, output_columns
WHERE name = 'nexyron.dataset_materialized_drop'
RETURN name, description, parameters, output_columns
```

Abstraction-platform runtime procedures build, materialize, refresh, inspect, and execute artifacts derived from registered abstraction contracts.

## Consumer-Level Explanation

This procedure is part of the abstraction artifact and refresh runtime. It manages built outputs, freshness, dependency visibility, or queued rebuild work rather than changing the underlying abstraction contract itself.

### Parameters

-   `name`: Dataset definition name Required.

### Output Columns

-   `name`
-   `scenario`
-   `dropped`

## Example Contract Prerequisite

The executable Cypher blocks on this page query `nexyron.procedures()` so they work in an empty database and stay synchronized with the live procedure registry. A direct `CALL nexyron.dataset_materialized_drop(...)` requires the named abstraction contracts, artifacts, features, snapshots, datasets, models, or policies referenced by that call to exist first; otherwise the runtime correctly fails with an unknown-contract error rather than inventing state.

## Conceptual Explanation

The important thing about `nexyron.dataset_materialized_drop` is that it stays inside the same Cypher and abstraction-contract runtime as the rest of Nexyron. That means it can compose with named abstractions, time-aware snapshot contracts, document-style payloads, graph-native identity, and later model or policy layers without forcing you to move into a separate tool first.

## More Detailed Explanation

In practical queries, start by deciding the row grain you want after the call: one row per node, one row per path, one row per registry object, one row per artifact, or one row per summary. Then keep that grain explicit with `YIELD` and named projections. That is the difference between a useful planner-facing example and a vague call that downstream tooling cannot safely compose. For contract-driven abstraction procedures, the executable examples on these pages intentionally inspect procedure metadata unless the required named artifacts are created in the same example.

## Advanced Example

```cypher
CALL nexyron.procedures()
YIELD name, parameters, output_columns
WHERE name = 'nexyron.dataset_materialized_drop'
RETURN name,
       [p IN parameters | p.name] AS parameter_names,
       output_columns
ORDER BY name
```

## Real Use Cases

-   Build or refresh quarter-specific training artifacts close to the graph so analysts can inspect the exact rows, labels, and split behavior before promoting a model.
-   Audit artifact freshness, dependency shape, and rebuild backlog when a late membership backfill or facility schedule correction invalidates downstream abstraction assets.

## Real Limitations And Tradeoffs

-   This surface is contract-driven. If the registered feature, snapshot, dataset, model, or policy definition is weak, the procedure will faithfully execute that weak contract rather than silently repairing it.
-   Materialized artifacts improve reuse, but they add freshness and invalidation concerns that on-demand execution avoids.