Iteration 2 - DATA_ENGINEER
Sequence: 4
Timestamp: 2025-07-25 22:29:20

Prompt:
You are a senior database architect implementing schema modifications for iteration 2. Based on the OR expert's optimization requirements and mapping analysis, you will design and implement the complete database architecture following industry best practices.

YOUR RESPONSIBILITIES:
- Analyze OR expert's mapping evaluations and missing requirements
- Design schema adjustments following database normalization principles
- Implement complete data dictionary with business-oriented descriptions
- Manage business configuration logic parameters (scalar values and formulas not suitable for tables)
- Maintain business realism by preserving relevant non-optimization tables
- Follow industry database design standards and naming conventions
- Ensure each table will store between 3 and 10 data rows for realistic optimization scenarios
- Apply the 3-row minimum rule - if optimization information is insufficient to generate at least 3 meaningful rows for a table, move that information to business_configuration_logic.json instead.


BUSINESS CONFIGURATION LOGIC DESIGN:
- Create business_configuration_logic.json for business parameters
- For scalar parameters: Use "sample_value" as templates for triple expert
- For business logic formulas: Use actual formula expressions (not "sample_value")
- Support different configuration_types:
  - "scalar_parameter": Single business values with "sample_value" (resources, limits, thresholds)
  - "business_logic_formula": Actual calculation formulas using real expressions
  - "business_metric": Performance evaluation metrics with "sample_value"
- Triple expert will later provide realistic values for scalar parameters only
- Formulas should be actual business logic expressions, not sample values


CRITICAL: Respond with ONLY a valid JSON object. No explanations, no markdown, no extra text.

OR EXPERT ANALYSIS (iteration 2):
{
  "database_id": "manufacturer",
  "iteration": 1,
  "business_context": "A furniture manufacturer aims to maximize its profit by determining the optimal number of each furniture type to produce, considering production costs, market rates, and factory capacity constraints.",
  "optimization_problem_description": "The manufacturer needs to decide how many units of each furniture type to produce to maximize profit, given the production costs, market rates, and limited factory capacity. The objective is to maximize the total profit, which is the difference between the revenue from selling the furniture and the production costs.",
  "optimization_formulation": {
    "objective": "maximize \u2211((Market_Rate - Price_in_Dollar) \u00d7 Quantity_Produced)",
    "decision_variables": "Quantity_Produced[Furniture_ID]: Number of units to produce for each furniture type",
    "constraints": "1. \u2211(Quantity_Produced) \u2264 Total_Factory_Capacity, 2. \u2211(Price_in_Dollar \u00d7 Quantity_Produced) \u2264 Budget_Limit"
  },
  "current_optimization_to_schema_mapping": {
    "objective_coefficients": {
      "Market_Rate[Furniture_ID]": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "Market rate for each furniture type"
      },
      "Price_in_Dollar[Furniture_ID]": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "Production cost for each furniture type"
      }
    },
    "constraint_bounds": {
      "Total_Factory_Capacity": {
        "currently_mapped_to": "business_configuration_logic.Total_Factory_Capacity",
        "mapping_adequacy": "good",
        "description": "Total production capacity of all factories"
      },
      "Budget_Limit": {
        "currently_mapped_to": "business_configuration_logic.Budget_Limit",
        "mapping_adequacy": "good",
        "description": "Total budget available for production"
      }
    },
    "decision_variables": {
      "Quantity_Produced[Furniture_ID]": {
        "currently_mapped_to": "production_plan.Quantity_Produced",
        "mapping_adequacy": "good",
        "description": "Number of units to produce for each furniture type",
        "variable_type": "integer"
      }
    }
  },
  "missing_optimization_requirements": [
    "Market_Rate[Furniture_ID]",
    "Price_in_Dollar[Furniture_ID]"
  ],
  "iteration_status": {
    "complete": false,
    "confidence": "medium",
    "next_focus": "Define and map the missing objective coefficients (Market_Rate and Price_in_Dollar) to complete the optimization model."
  }
}


CURRENT DATABASE SCHEMA:
```sql
-- Iteration 1 Database Schema
-- Objective: Schema changes include creating tables for decision variables and constraint bounds, and updating business configuration logic to include scalar parameters and formulas for missing optimization requirements.

CREATE TABLE production_plan (
  Furniture_ID INTEGER,
  Quantity_Produced INTEGER
);


```


CURRENT DATA DICTIONARY:
{
  "tables": {
    "production_plan": {
      "business_purpose": "Number of units to produce for each furniture type",
      "optimization_role": "decision_variables",
      "columns": {
        "Furniture_ID": {
          "data_type": "INTEGER",
          "business_meaning": "Unique identifier for each furniture type",
          "optimization_purpose": "Index for decision variables",
          "sample_values": "1, 2, 3"
        },
        "Quantity_Produced": {
          "data_type": "INTEGER",
          "business_meaning": "Number of units to produce for each furniture type",
          "optimization_purpose": "Decision variable in optimization model",
          "sample_values": "10, 20, 30"
        }
      }
    }
  }
}


CURRENT BUSINESS CONFIGURATION LOGIC:
{
  "Total_Factory_Capacity": {
    "sample_value": 1000,
    "data_type": "INTEGER",
    "business_meaning": "Total production capacity of all factories",
    "optimization_role": "constraint bound",
    "configuration_type": "scalar_parameter"
  },
  "Budget_Limit": {
    "sample_value": 50000,
    "data_type": "FLOAT",
    "business_meaning": "Total budget available for production",
    "optimization_role": "constraint bound",
    "configuration_type": "scalar_parameter"
  }
}


TASK: Implement comprehensive schema changes and configuration logic management based on OR expert's requirements.

JSON STRUCTURE REQUIRED:

{
  "database_id": "manufacturer",
  "iteration": 2,
  "implementation_summary": "Summary of schema changes and configuration logic updates based on OR expert mapping analysis",
  
  "or_requirements_analysis": {
    "mapping_gaps_identified": [
      "List specific gaps identified from OR expert's mapping_adequacy assessments"
    ],
    "missing_data_requirements": [
      "List missing optimization data requirements from OR expert"
    ],
    "business_configuration_logic_needs": [
      "Scalar parameters and formulas better suited for configuration than tables"
    ]
  },
  
  "schema_adjustment_decisions": {
    "tables_to_delete": [
      {
        "table_name": "table_name",
        "reason": "business justification for removal (optimization irrelevant vs business irrelevant)"
      }
    ],
    "tables_to_create": [
      {
        "table_name": "table_name", 
        "purpose": "optimization role (decision_variables/objective_coefficients/constraint_bounds/business_data)",
        "business_meaning": "what this table represents in business context"
      }
    ],
    "tables_to_modify": [
      {
        "table_name": "existing_table",
        "changes": "specific modifications needed",
        "reason": "why these changes address OR expert's mapping gaps"
      }
    ]
  },
  
  "business_configuration_logic_updates": {
    "configuration_parameters": {
      "parameter_name": {
        "sample_value": "sample_parameter_value",
        "data_type": "INTEGER/FLOAT/STRING/BOOLEAN",
        "business_meaning": "what this parameter represents in business context",
        "optimization_role": "how this parameter is used in optimization model",
        "configuration_type": "scalar_parameter"
      },
      "formula_name": {
        "formula_expression": "actual_business_formula_expression",
        "data_type": "STRING",
        "business_meaning": "what this formula represents in business context",
        "optimization_role": "how this formula is used in optimization model",
        "configuration_type": "business_logic_formula"
      }
    },
    "updates_rationale": "why these parameters are better in configuration logic than tables"
  },
  
  "implemented_optimization_mapping": {
    "objective_coefficients_mapping": {
      "coefficient_name[indices]": "table.column OR business_configuration_logic.key - complete mapping path"
    },
    "constraint_bounds_mapping": {
      "constraint_name[indices]": "table.column OR business_configuration_logic.key - complete mapping path"  
    },
    "decision_variables_mapping": {
      "variable_name[indices]": "table.column OR business_configuration_logic.key - complete mapping path"
    }
  },
  
  "data_dictionary": {
    "tables": {
      "table_name": {
        "business_purpose": "business meaning of this table for linear optimization",
        "optimization_role": "decision_variables/objective_coefficients/constraint_bounds/business_data",
        "columns": {
          "column_name": {
            "data_type": "TYPE",
            "business_meaning": "what this column represents in business context for linear optimization",
            "optimization_purpose": "how this column is used in linear optimization formulation",
            "sample_values": "example values or range"
          }
        }
      }
    }
  },
  
  "data_mapping": {
    "objective_sources": ["table.column pairs for linear objective function"],
    "constraint_sources": ["table.column pairs for linear constraints"],
    "sample_data_rows": {"table1": 3, "table2": 5}
  },
  
  "validation": {
    "schema_complete": true,
    "data_consistent": true,
    "math_traceable": true,
    "business_aligned": true,
    "follows_db_standards": true
  }
}

Response:
{
  "database_id": "manufacturer",
  "iteration": 2,
  "implementation_summary": "Schema changes include creating tables for objective coefficients (Market_Rate and Price_in_Dollar) and updating business configuration logic to include scalar parameters and formulas for missing optimization requirements.",
  "or_requirements_analysis": {
    "mapping_gaps_identified": [
      "Market_Rate[Furniture_ID]",
      "Price_in_Dollar[Furniture_ID]"
    ],
    "missing_data_requirements": [
      "Market_Rate[Furniture_ID]",
      "Price_in_Dollar[Furniture_ID]"
    ],
    "business_configuration_logic_needs": [
      "Scalar parameters and formulas better suited for configuration than tables"
    ]
  },
  "schema_adjustment_decisions": {
    "tables_to_delete": [],
    "tables_to_create": [
      {
        "table_name": "furniture_market_rates",
        "purpose": "objective_coefficients",
        "business_meaning": "Market rate for each furniture type"
      },
      {
        "table_name": "furniture_production_costs",
        "purpose": "objective_coefficients",
        "business_meaning": "Production cost for each furniture type"
      }
    ],
    "tables_to_modify": []
  },
  "business_configuration_logic_updates": {
    "configuration_parameters": {
      "Total_Factory_Capacity": {
        "sample_value": 1000,
        "data_type": "INTEGER",
        "business_meaning": "Total production capacity of all factories",
        "optimization_role": "constraint bound",
        "configuration_type": "scalar_parameter"
      },
      "Budget_Limit": {
        "sample_value": 50000,
        "data_type": "FLOAT",
        "business_meaning": "Total budget available for production",
        "optimization_role": "constraint bound",
        "configuration_type": "scalar_parameter"
      }
    },
    "updates_rationale": "These parameters are better suited for configuration logic as they are scalar values and do not require table storage."
  },
  "implemented_optimization_mapping": {
    "objective_coefficients_mapping": {
      "Market_Rate[Furniture_ID]": "furniture_market_rates.Market_Rate",
      "Price_in_Dollar[Furniture_ID]": "furniture_production_costs.Price_in_Dollar"
    },
    "constraint_bounds_mapping": {
      "Total_Factory_Capacity": "business_configuration_logic.Total_Factory_Capacity",
      "Budget_Limit": "business_configuration_logic.Budget_Limit"
    },
    "decision_variables_mapping": {
      "Quantity_Produced[Furniture_ID]": "production_plan.Quantity_Produced"
    }
  },
  "data_dictionary": {
    "tables": {
      "production_plan": {
        "business_purpose": "Number of units to produce for each furniture type",
        "optimization_role": "decision_variables",
        "columns": {
          "Furniture_ID": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for each furniture type",
            "optimization_purpose": "Index for decision variables",
            "sample_values": "1, 2, 3"
          },
          "Quantity_Produced": {
            "data_type": "INTEGER",
            "business_meaning": "Number of units to produce for each furniture type",
            "optimization_purpose": "Decision variable in optimization model",
            "sample_values": "10, 20, 30"
          }
        }
      },
      "furniture_market_rates": {
        "business_purpose": "Market rate for each furniture type",
        "optimization_role": "objective_coefficients",
        "columns": {
          "Furniture_ID": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for each furniture type",
            "optimization_purpose": "Index for objective coefficients",
            "sample_values": "1, 2, 3"
          },
          "Market_Rate": {
            "data_type": "FLOAT",
            "business_meaning": "Market rate for each furniture type",
            "optimization_purpose": "Objective coefficient in optimization model",
            "sample_values": "100.0, 150.0, 200.0"
          }
        }
      },
      "furniture_production_costs": {
        "business_purpose": "Production cost for each furniture type",
        "optimization_role": "objective_coefficients",
        "columns": {
          "Furniture_ID": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for each furniture type",
            "optimization_purpose": "Index for objective coefficients",
            "sample_values": "1, 2, 3"
          },
          "Price_in_Dollar": {
            "data_type": "FLOAT",
            "business_meaning": "Production cost for each furniture type",
            "optimization_purpose": "Objective coefficient in optimization model",
            "sample_values": "50.0, 75.0, 100.0"
          }
        }
      }
    }
  },
  "data_mapping": {
    "objective_sources": [
      "furniture_market_rates.Market_Rate",
      "furniture_production_costs.Price_in_Dollar"
    ],
    "constraint_sources": [
      "business_configuration_logic.Total_Factory_Capacity",
      "business_configuration_logic.Budget_Limit"
    ],
    "sample_data_rows": {
      "production_plan": 3,
      "furniture_market_rates": 3,
      "furniture_production_costs": 3
    }
  },
  "validation": {
    "schema_complete": true,
    "data_consistent": true,
    "math_traceable": true,
    "business_aligned": true,
    "follows_db_standards": true
  }
}
