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:

# 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

# 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):

# 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):

-- 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:

-- 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:

-- 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:

# 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