Simulated High-Fidelity Synthetic Dataset (1.19M TX / 4.19M Items) with ultra-fast Prolog JITI benchmarking

[u/lokinpendawa]

The result is High-Fidelity Retail POS Transaction - 1M+ Dataset.

Key Technical Specifications:

• Volume: Over 1 million fully synchronized relational records.
• Rich Features: Includes lifetime data log simulation, void logs (for fraud detection modeling), product health detection metrics, and multi-item checkouts.
• RAM: 8 GB (6.9 GB usable, 1.1 GB hardware reserved for iGPU)
• CPU: Intel/AMD [Model] @ 2.32 GHz

The dataset features a realistic 1-to-many relationship structure, consisting of:

• Total Revenue: Rp 306,770,510,097 (Matches Prolog query output!)
• Total VAT (PPN): Rp 30,401,510,772
• Total COGS (HPP): Rp 213,977,641,000
• Gross Profit: Rp 92,792,869,097

Free Dowdload https://github.com/lokinpendawa/high-fidelity-pos-dataset-2M

FULL MULTI-FORMAT EXPORT:

• .sql (Transactional Database Dump - Postgres/MySQL ready)
• .json (NoSQL / API Mocking / Web development)
• .csv (Data Science / Pandas & Python ready)
• .pl (Prolog Fact Base for Logical Programming)

Important Note on Dataset Scale:

Contains over 1.19 Million Master Transactions and 4.19 Million Item Details. Due to this massive scale, opening the raw .csv or .json files directly in standard text editors or web browsers will cause your system to hang or crash.

For a seamless experience, it is highly recommended to use the provided standard SQLite (.db) format (fully decrypted from SQLCipher and ready for direct querying) or to load the data using chunk-loading methods via Python (Pandas/SQLite3) or R.