Bigquery Asi Interleave

---
name: bigquery-asi-interleave
description: Interleave layer bridging the BigQuery cluster to plurigrid/asi. Routes BigQuery queries through asi's DuckDB stack, wires patent search into asi knowledge graph, connects Looker Studio dashboards to CatColab, and feeds BigQuery ML into the lolita physics emulation pipeline.
version: 1.0.0
trit: 0
role: BRIDGE
tags: [bigquery, asi, duckdb, patent, looker-studio, gf3, interleave]
deployed: 2026-02-19
---

# BigQuery × ASI Interleave

Bridge layer connecting the 6-skill BigQuery cluster to plurigrid/asi's 1360+ skill graph.

## Skill Cluster Map

```
bigquery (trit:0, comprehensive)          ← hub: bq CLI, GoogleSQL, ML, governance
  ├── bigquery-table-creator (-1)          ← infra: DDL, partitioned/clustered tables
  ├── restricted-bigquery-dbt-environment (-1) ← safety: dbt test schema guard
  ├── bigquery-patent-search (0)           ← bridge: 76M+ patent corpus via BQ public data
  ├── looker-studio-bigquery (0)           ← bridge: Looker Studio dashboards
  └── bigquery-table-creator (+1)          ← orchestration: GCP table lifecycle
```

## GF(3) Tripartite

`bigquery-table-creator(-1) ⊗ bigquery-asi-interleave(0) ⊗ looker-studio-bigquery(+1) = 0`

Infrastructure DDL (-1) × Bridge (0) × Visualization (+1) = balanced data stack.

## ASI Integration Points

### 1. BigQuery ↔ DuckDB — Cloud/Local Hybrid Query

asi already has rich DuckDB: `duckdb-ies`, `duckdb-spatial`, `duckdb-quadruple-interleave`,
`duckdb-timetravel`, `duckdb-temporal-versioning`.

BigQuery is the cloud-scale complement:

```bash
# Export BQ → DuckDB for local analysis
bq extract --destination_format=PARQUET \
  'project:dataset.table' gs://bucket/export/*.parquet

# Load into DuckDB for local temporal analysis
duckdb asi.db << 'EOF'
INSTALL httpfs; LOAD httpfs;
CREATE TABLE bq_export AS
  SELECT * FROM read_parquet('gs://bucket/export/*.parquet');
-- Now apply duckdb-timetravel patterns locally
EOF
```

```bash
# Inverse: push DuckDB results to BigQuery
duckdb asi.db -c "COPY (SELECT * FROM skill_graph) TO '/tmp/skills.parquet' (FORMAT PARQUET)"
bq load --source_format=PARQUET project:asi_dataset.skill_graph /tmp/skills.parquet
```

Pattern: BQ = warehouse (PB scale), DuckDB = analytical engine (GB scale), asi = skill graph on top.

### 2. Patent Search → ASI Knowledge Graph

`bigquery-patent-search` queries `patents-public-data.patents` (76M+ patents):

```python
# Search for prior art on asi's core concepts
from python.bigquery_search import BigQueryPatentSearch
searcher = BigQueryPatentSearch()

# GF(3) / ternary computing patents
gf3_patents = searcher.search_patents(
    query="ternary logic GF(3) color semantics",
    cpc_prefix="G06F",  # Computing
    start_year=2010
)

# OCapN / capability-secure networking
ocapn_patents = searcher.search_patents(
    query="object capability network distributed computing",
    cpc_prefix="H04L",  # Digital communication
)

# Latent diffusion physics emulation (lolita)
lolita_priors = searcher.search_patents(
    query="latent diffusion physics simulation neural operator",
    cpc_prefix="G06N",  # ML/neural
    start_year=2020
)
```

Wire results into `openalex-database` + `hatchery-papers` for full prior art graph.

### 3. BigQuery ML → Lolita Physics Pipeline

BigQuery ML complements the Vertex AI pipeline (lolita, task#23):

```sql
-- Train a forecasting model on attractor time series (dysts corpus)
CREATE OR REPLACE MODEL `asi_project.physics.attractor_forecast`
OPTIONS (
  model_type = 'ARIMA_PLUS',
  time_series_timestamp_col = 'timestep',
  time_series_data_col = 'value',
  time_series_id_col = 'attractor_name',
  auto_arima = TRUE,
  data_frequency = 'AUTO_FREQUENCY'
) AS
SELECT timestep, value, attractor_name
FROM `asi_project.physics.dysts_trajectories`;

-- Predict next 100 steps
SELECT *
FROM ML.FORECAST(
  MODEL `asi_project.physics.attractor_forecast`,
  STRUCT(100 AS horizon, 0.9 AS confidence_level)
);
```

Route predictions back to `lolita` (latent diffusion) as warm-start priors.

### 4. Looker Studio → CatColab Dashboard

`looker-studio-bigquery` + `catcolab-stock-flow` + `catcolab-causal-loop`:

F-pattern dashboard for asi skill graph health:

```sql
-- Skill graph daily metrics (feeds Looker Studio)
CREATE OR REPLACE TABLE `asi_project.dashboard.skill_metrics` AS
SELECT
  CURRENT_DATE() as report_date,
  COUNT(*) as total_skills,
  COUNTIF(trit = -1) as negative_skills,
  COUNTIF(trit = 0) as neutral_skills,
  COUNTIF(trit = 1) as positive_skills,
  -- MONOTONIC_SKILL_INVARIANT
  CASE WHEN COUNT(*) >= 1360 THEN TRUE ELSE FALSE END as invariant_holds
FROM `asi_project.skills.registry`;
```

KPI tiles: total skills (≥1360), GF(3) trit distribution, hub reachability (17 hubs).

### 5. dbt Safety → ASI Skill Safety

`restricted-bigquery-dbt-environment` pattern applied to asi skill writes:

```sql
-- SAFE: Always write to test schema first
{{ config(
    schema='asi_test',  -- <- ALWAYS during development
    materialized='incremental',
    unique_key='skill_name'
) }}

SELECT * FROM {{ ref('skill_candidates') }}
WHERE validated = TRUE
  AND trit_balance = 0  -- GF(3) invariant
```

Rule: **NEVER commit skill writes without `schema='asi_test'` removed**.
Run `git diff` before push to verify MONOTONIC_SKILL_INVARIANT preserved.

### 6. Skill Prior Art Search — asi × uspto-database

Connect `bigquery-patent-search` to `uspto-database` for full prior art:

```python
# Find patent landscape around key asi concepts
concepts = [
    ("topological chemputer CRN", "C07", "Chemical reactions"),
    ("distributed capability object coloring", "H04L", "Networks"),
    ("GF(3) ternary neural network", "G06N", "ML"),
    ("category theory compositional game", "G06F", "Computing"),
    ("latent diffusion physics operator", "G06N", "ML"),
]

for query, cpc, domain in concepts:
    results = searcher.search_patents(query=query, cpc_prefix=cpc, limit=5)
    print(f"\n=== {domain}: {query} ===")
    for r in results:
        print(f"  {r['publication_number']}: {r['title'][:60]}")
```

Use results to identify white space for asi's novel contributions.

## Security Notes

- `restricted-bigquery-dbt-environment`: NEVER run `dbt run` without `schema='test'`
- All BQ queries: use `--dry_run` to estimate cost before large scans
- IAM: `bigquery.dataViewer` minimum; `bigquery.jobUser` for queries
- Patent data is public — no auth needed for `patents-public-data.*`
- Free tier: 1TB/month queries free; ~20,000 patent searches/month free

## Related ASI Skills

- `duckdb-ies` / `duckdb-quadruple-interleave` — local DuckDB complement to BQ warehouse
- `lolita` / task#23 — physics emulation pipeline fed by BQML forecasting
- `bigquery-patent-search` → `uspto-database` + `openalex-database` + `hatchery-papers`
- `catcolab-stock-flow` / `catcolab-causal-loop` — Looker Studio → CatColab olog export
- `vertex-asi-interleave` — parent GCP interleave (BigQuery lives inside the same GCP project)
- `wolframite-compass` — Wolfram data alongside BigQuery public datasets
- `restricted-bigquery-dbt-environment` → model safety pattern for all asi data writes