Architecture Discussion

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Core Components

• Schema Parser and Validator: Takes user-provided schemas (JSON or SQL DDL) as input and outputs a structured representation of the schema, such as an abstract syntax tree (AST) or an internal data model.
• Condition Engine: Receives the parsed schema and user-defined conditions, then processes these conditions to output constraints and rules that the Data Generator will use.
• Data Generator: Consumes the structured schema and the processed conditions to generate synthetic data that adheres to both.
• Output Formatter: Formats generated data according to the required structure (e.g., CSV, JSON, SQL).

Storage:

• Schema Repository: To store and retrieve user-provided schemas.
• Condition Repository: To manage custom conditions and rules.

User Interface:

• Web-Based UI: For user input, schema upload, and setting conditions.
• API Interface: For programmatic access to the synthetic data generation service.

Integration Module:

• Database Integrator: For generating synthetic data directly into databases.

Security:

Ensure that user data and custom conditions are handled securely and comply with relevant data protection regulations.

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Data Flow and Interaction Sequence

Schema Parser:

• User Input: User provides a schema file (e.g., a JSON schema).
• Schema Parser: The schema parser reads the schema, validates its structure, and converts it into an internal data model that includes field types, constraints, relationships, and other metadata..
• Output: The structured schema as data model is passed to the Condition Engine.

Condition Engine :

• User Input: User defines conditions and rules (e.g., age must be between 20 and 50, name should be unique).
• Condition Engine:
  • The Condition Engine receives the structured schema
  • Condition engine parses and interprets Conditions
  • It validates the conditions against the schema, ensuring they are logical and applicable.
  • It then processes these conditions into a set of actionable constraints or rules.
• Output: The processed constraints are passed to the Data Generator.

Data Generator :

• Receives the structured schema and processed constraints.
• Uses this information to generate synthetic data that adheres to the schema and meets the specified conditions.

Output Formatter :

• Receives the data output from Data Generator and formats it in the required format such as JSON, XML, SQL

Error Handling and Feedback Mechanisms:

• Schema Validation : If the schema parser encounters errors (e.g., incorrect schema format), it should provide detailed feedback to the user.

• Condition Validation: The Condition Engine should handle conflicts or invalid conditions, providing warnings or errors back to the user.

• Iterative Refinement : Feedback Loop: If generated data doesn't meet the conditions, the engine provides feedback to the Data Generator to adjust its generation process, ensuring the final output adheres to all conditions.

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Integration of ML Model with Schema Parser and Condition Engine

• Schema Parsing and Preprocessing :

  Feature Representation: Map schema fields to features that can be used by the ML model. For instance:

  • Numerical fields -> Continuous features.
  • Categorical fields -> One-hot or label-encoded features.
  • Text fields -> Tokenized or embedded features.
  • Date/Time fields -> Converted to numerical features (e.g., timestamps, differences).

• Condition Processing

  • Combine the schema information with the conditions to define how the data should be represented. For example:

    • Ensure that numerical features respect the specified ranges.
    • Ensure categorical distributions match the specified probabilities.
    • Maintain inter-field dependencies (e.g., if "End Date" must be after "Start Date").

  • Feature Constraints

    • Translate conditions into feature-level constraints that can be understood by the ML model (e.g., conditional probability distributions, feature correlations).