Procurement Policy Exception Detection SQL Dataset

#SQL generation #Database schema understanding #SQL query analysis #Procurement exception detection #Procurement compliance monitoring #Spend risk review #Supplier auditing #Procurement process analysis

Records: 4130
Size: 0.5GB
Format: JSON
Provider: MOBIUSI INC
Updated: v1.02026-11-05

AI Analysis & Value Prop

This dataset focuses on procurement controls and spend management, pairing natural-language questions with SQL queries and database schema information. It covers policy exception checks such as duplicate invoices, suspected split purchases, and purchases that bypass preferred procurement channels. Metadata describes exception categories, referenced tables and columns, detection logic, joins and aggregations, policy thresholds, and expected outcomes, supporting SQL Agent training and evaluation for procurement policy monitoring and risk review.

Overview

Dataset Insights

Preview

Sample Examples

sample.json

{}

Schema

Technical Specifications

Field	Type	Description
file_name	string	File name
source_sql	string	The original SQL query intended to identify or analyze the procurement policy exception.
query_pattern	string	The primary SQL analysis pattern used to implement the exception check.
source_question	string	The original natural-language question describing a procurement policy exception to detect.
policy_thresholds	string	Relevant procurement policy limits or threshold conditions expressed in the question or query.
referenced_tables	string	The database tables used by the SQL query, represented as a comma-separated list of table names.
exception_category	string	The primary procurement exception category addressed by the question and query.
referenced_columns	string	The key database columns used for filtering, joining, grouping, or comparison, represented as a comma-separated list.
source_database_schema	string	The source database schema and relevant table or column definitions, serialized as JSON text.
detection_logic_summary	string	A concise explanation of how the query identifies the targeted procurement exception.
expected_detection_outcome	string	A description of the records or conditions the query is expected to return.
join_and_aggregation_logic	string	A summary of the joins, grouping, counts, sums, or other aggregation operations used by the query.

Trust

Compliance Statement

Authorization Type

Proprietary - Commercial AI Training License (No Redistribution)

Commercial Use

Requires exclusive subscription or authorization contract (monthly or per-invocation charging)

Privacy and Anonymization

No PII, no real company names, simulated scenarios follow industry standards

Compliance System

Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs

FAQ

Frequently Asked Questions

What does the dataset contain?: It includes natural-language questions about procurement policy exceptions, corresponding SQL queries, and database schema information. Additional metadata covers exception categories, referenced tables and columns, detection logic, joins and aggregations, policy thresholds, and expected detection outcomes.

Which capabilities can this dataset support?: It can support training or evaluation of natural-language-to-SQL generation, database schema understanding, table and column linking, query analysis, and procurement exception detection. Whether a query runs as-is depends on the target database schema, SQL dialect, and data.

Which procurement exceptions are covered?: The reference content includes duplicate invoices, suspected split purchases, and purchases that bypass established procurement channels. These examples are relevant to policy monitoring and risk review in spend management software.

How can I access the data, and what are the licensing terms?: Use the delivery channel provided on the dataset product page or platform. Available formats, access methods, and permitted uses depend on the listing and applicable license; review those terms before use.

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Cite this Work

@dataset{Mobiusi2026,
  title={Procurement Policy Exception Detection SQL Dataset},
  author={MOBIUSI INC},
  year={2026},
  url={https://www.mobiusi.com/datasets/ddf32392689075b1b22a8fb5cea075cd?dataset_scene_id=3},
  urldate={2026-10-22},
  keywords={procurement policy exception dataset,procurement SQL dataset,SQL Agent training,procurement compliance monitoring,duplicate invoice detection,split purchase detection,spend management data,procurement anomaly detection},
  version={1.0}
}

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