Iteration 1 - DATA_ENGINEER
Sequence: 2
Timestamp: 2025-07-27 23:29:46

Prompt:
You are a senior database architect implementing schema modifications for iteration 1. 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 20 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 1):
{
  "database_id": "school_finance",
  "iteration": 0,
  "business_context": "A school district wants to optimize the allocation of its budget across different schools to maximize the total invested amount while ensuring that each school receives a minimum percentage of its budgeted amount.",
  "optimization_problem_description": "The goal is to maximize the total invested amount across all schools while ensuring that each school receives at least a certain percentage of its budgeted amount. The decision variables are the amounts to be invested in each school, and the constraints ensure that the investments do not exceed the budgeted amounts and meet the minimum investment requirements.",
  "optimization_formulation": {
    "objective": "maximize total_invested = \u2211(Invested_i) for all schools i",
    "decision_variables": "Invested_i: amount to be invested in school i (continuous)",
    "constraints": [
      "Invested_i \u2264 Budgeted_i for all schools i",
      "Invested_i \u2265 min_investment_percent * Budgeted_i for all schools i"
    ]
  },
  "current_optimization_to_schema_mapping": {
    "objective_coefficients": {
      "Invested[i]": {
        "currently_mapped_to": "budget.Invested",
        "mapping_adequacy": "good",
        "description": "amount invested in school i"
      }
    },
    "constraint_bounds": {
      "Budgeted[i]": {
        "currently_mapped_to": "budget.Budgeted",
        "mapping_adequacy": "good",
        "description": "total budgeted amount for school i"
      },
      "min_investment_percent": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "minimum percentage of the budgeted amount that must be invested in each school"
      }
    },
    "decision_variables": {
      "Invested[i]": {
        "currently_mapped_to": "budget.Invested",
        "mapping_adequacy": "good",
        "description": "amount to be invested in school i",
        "variable_type": "continuous"
      }
    }
  },
  "missing_optimization_requirements": [
    "min_investment_percent: Minimum percentage of the budgeted amount that must be invested in each school"
  ],
  "iteration_status": {
    "complete": false,
    "confidence": "medium",
    "next_focus": "Determine the specific minimum investment percentage required for each school"
  }
}





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

JSON STRUCTURE REQUIRED:

{
  "database_id": "school_finance",
  "iteration": 1,
  "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": "school_finance",
  "iteration": 1,
  "implementation_summary": "Schema adjustments include adding missing configuration logic for minimum investment percentage and ensuring all optimization requirements are mapped correctly. Business configuration logic is updated to include scalar parameters and formulas.",
  "or_requirements_analysis": {
    "mapping_gaps_identified": [
      "min_investment_percent is missing from the current schema"
    ],
    "missing_data_requirements": [
      "min_investment_percent: Minimum percentage of the budgeted amount that must be invested in each school"
    ],
    "business_configuration_logic_needs": [
      "min_investment_percent is better suited as a scalar parameter in configuration logic"
    ]
  },
  "schema_adjustment_decisions": {
    "tables_to_delete": [],
    "tables_to_create": [],
    "tables_to_modify": [
      {
        "table_name": "budget",
        "changes": "Add column for min_investment_percent",
        "reason": "To address the missing mapping for minimum investment percentage"
      }
    ]
  },
  "business_configuration_logic_updates": {
    "configuration_parameters": {
      "min_investment_percent": {
        "sample_value": "0.1",
        "data_type": "FLOAT",
        "business_meaning": "Minimum percentage of the budgeted amount that must be invested in each school",
        "optimization_role": "Used as a constraint in the optimization model",
        "configuration_type": "scalar_parameter"
      }
    },
    "updates_rationale": "min_investment_percent is a scalar parameter that is more efficiently managed in configuration logic than in a table due to its singular value nature."
  },
  "implemented_optimization_mapping": {
    "objective_coefficients_mapping": {
      "Invested[i]": "budget.Invested"
    },
    "constraint_bounds_mapping": {
      "Budgeted[i]": "budget.Budgeted",
      "min_investment_percent": "business_configuration_logic.min_investment_percent"
    },
    "decision_variables_mapping": {
      "Invested[i]": "budget.Invested"
    }
  },
  "data_dictionary": {
    "tables": {
      "budget": {
        "business_purpose": "Stores budget and investment data for each school",
        "optimization_role": "decision_variables/constraint_bounds",
        "columns": {
          "Invested": {
            "data_type": "FLOAT",
            "business_meaning": "Amount invested in school i",
            "optimization_purpose": "Decision variable representing investment amount",
            "sample_values": "1000.0, 2000.0, 3000.0"
          },
          "Budgeted": {
            "data_type": "FLOAT",
            "business_meaning": "Total budgeted amount for school i",
            "optimization_purpose": "Constraint bound for maximum investment",
            "sample_values": "5000.0, 6000.0, 7000.0"
          },
          "min_investment_percent": {
            "data_type": "FLOAT",
            "business_meaning": "Minimum percentage of budget that must be invested",
            "optimization_purpose": "Constraint bound for minimum investment",
            "sample_values": "0.1, 0.15, 0.2"
          }
        }
      }
    }
  },
  "data_mapping": {
    "objective_sources": [
      "budget.Invested"
    ],
    "constraint_sources": [
      "budget.Budgeted",
      "business_configuration_logic.min_investment_percent"
    ],
    "sample_data_rows": {
      "budget": 3
    }
  },
  "validation": {
    "schema_complete": true,
    "data_consistent": true,
    "math_traceable": true,
    "business_aligned": true,
    "follows_db_standards": true
  }
}
