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Dataset Storage Analytics — String Normalisation + Two-Measure Correlation
x402
Dataset Storage Analytics — String Normalisation + Two-Measure Correlation is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-19).
Runs string normalisation and Pearson/rank correlation analysis between used_mb and row_count fields on up to 200 dataset storage records, returning two analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Dataset storage — String normalisation + Two-measure correlation: String normalisation + Two-measure correlation for supplied dataset storage records (used_mb, row_count). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.
Output
Returns two analytical reports: (1) a string normalisation report cleaning and standardising the dataset_group text fields, and (2) a two-measure correlation report quantifying the statistical relationship between used_mb and row_count across the submitted rows. No external lookups or predictions are made — all analysis is performed locally on the supplied data.
Example use cases
Storage growth correlation audit
“I have 120 rows of dataset storage logs with used_mb and row_count per dataset group and date. Can you normalize the group name strings and tell me whether storage size in MB is statistically correlated with row count?”
Data pipeline storage analysis
“After our ETL pipeline ran last month, I collected daily storage readings across several dataset groups — can you clean up the group name strings and compute the correlation between how many MBs are used versus how many rows exist for each record? I'll send you up to 200 rows.”
Multi-group dataset quality check
“I want to understand the relationship between row_count and used_mb across my different dataset groups. Please normalize the group label strings and give me a correlation report — I've got about 80 records with date, row_id, dataset_group, used_mb, and row_count.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-18-033?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to simultaneously clean/normalise categorical string fields (dataset_group names) and compute a statistical correlation between two numeric storage measures (used_mb and row_count) on up to 200 rows of dataset storage records. Prefer this over generic analytics APIs when your data specifically involves storage metrics with the exact fields: row_id, dataset_group, date, used_mb, row_count. This is a local, offline computation — no data leaves to third-party prediction or lookup services.
Side effects
- Fewer than 1 row submitted — minimum 1 row required
- More than 200 rows submitted — maxItems limit exceeded
- Missing required fields (row_id, dataset_group, date, used_mb, or row_count) causing validation error
- Invalid date format (must match YYYY-MM-DD pattern)
- Null values in both numeric measures may reduce correlation reliability
- row_id or dataset_group strings exceeding 80 characters
- Insufficient numeric variance in used_mb or row_count to compute meaningful correlation
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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