Iteration 1 - DATA_ENGINEER
Sequence: 2
Timestamp: 2025-07-25 22:36:07

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 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 1):
{
  "database_id": "employee_hire_evaluation",
  "iteration": 0,
  "business_context": "A retail chain aims to optimize employee hiring and allocation across shops to minimize total hiring costs while ensuring each shop has sufficient staff and employees are fairly evaluated based on their bonuses.",
  "optimization_problem_description": "The objective is to minimize the total hiring costs by deciding the number of full-time and part-time employees to hire for each shop, ensuring that each shop meets its staffing requirements and that employees receive bonuses based on their evaluations.",
  "optimization_formulation": {
    "objective": "minimize \u2211(cost_full_time \u00d7 x_full_time[Shop_ID] + cost_part_time \u00d7 x_part_time[Shop_ID])",
    "decision_variables": {
      "x_full_time[Shop_ID]": "Number of full-time employees to hire for each shop (integer)",
      "x_part_time[Shop_ID]": "Number of part-time employees to hire for each shop (integer)"
    },
    "constraints": [
      "x_full_time[Shop_ID] + x_part_time[Shop_ID] \u2265 min_staff[Shop_ID] for each shop",
      "x_full_time[Shop_ID] \u2264 max_full_time[Shop_ID] for each shop",
      "x_part_time[Shop_ID] \u2264 max_part_time[Shop_ID] for each shop",
      "\u2211(bonus[Employee_ID] \u00d7 y[Employee_ID]) \u2264 total_bonus_budget",
      "y[Employee_ID] \u2264 1 for each employee (binary decision to award bonus)"
    ]
  },
  "current_optimization_to_schema_mapping": {
    "objective_coefficients": {
      "cost_full_time": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "Cost of hiring a full-time employee"
      },
      "cost_part_time": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "Cost of hiring a part-time employee"
      }
    },
    "constraint_bounds": {
      "min_staff[Shop_ID]": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "Minimum staff required for each shop"
      },
      "max_full_time[Shop_ID]": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "Maximum full-time employees allowed for each shop"
      },
      "max_part_time[Shop_ID]": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "Maximum part-time employees allowed for each shop"
      },
      "total_bonus_budget": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "Total budget allocated for employee bonuses"
      }
    },
    "decision_variables": {
      "x_full_time[Shop_ID]": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "Number of full-time employees to hire for each shop",
        "variable_type": "integer"
      },
      "x_part_time[Shop_ID]": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "Number of part-time employees to hire for each shop",
        "variable_type": "integer"
      },
      "y[Employee_ID]": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "Binary decision to award bonus to an employee",
        "variable_type": "binary"
      }
    }
  },
  "missing_optimization_requirements": [
    "Cost of hiring full-time and part-time employees",
    "Minimum staff required for each shop",
    "Maximum full-time and part-time employees allowed for each shop",
    "Total budget allocated for employee bonuses"
  ],
  "iteration_status": {
    "complete": false,
    "confidence": "medium",
    "next_focus": "Define and map the missing coefficients and constraints to the schema or business configuration logic."
  }
}





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

JSON STRUCTURE REQUIRED:

{
  "database_id": "employee_hire_evaluation",
  "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": "employee_hire_evaluation",
  "iteration": 1,
  "implementation_summary": "Schema changes include creating new tables for decision variables, objective coefficients, and constraint bounds. Business configuration logic updated with scalar parameters and formulas for optimization model.",
  "or_requirements_analysis": {
    "mapping_gaps_identified": [
      "cost_full_time, cost_part_time, min_staff[Shop_ID], max_full_time[Shop_ID], max_part_time[Shop_ID], total_bonus_budget"
    ],
    "missing_data_requirements": [
      "Cost of hiring full-time and part-time employees",
      "Minimum staff required for each shop",
      "Maximum full-time and part-time employees allowed for each shop",
      "Total budget allocated for employee bonuses"
    ],
    "business_configuration_logic_needs": [
      "cost_full_time, cost_part_time, total_bonus_budget"
    ]
  },
  "schema_adjustment_decisions": {
    "tables_to_delete": [],
    "tables_to_create": [
      {
        "table_name": "shop_staff_requirements",
        "purpose": "constraint_bounds",
        "business_meaning": "Minimum and maximum staff requirements for each shop"
      },
      {
        "table_name": "employee_bonus_decisions",
        "purpose": "decision_variables",
        "business_meaning": "Binary decisions to award bonuses to employees"
      }
    ],
    "tables_to_modify": []
  },
  "business_configuration_logic_updates": {
    "configuration_parameters": {
      "cost_full_time": {
        "sample_value": 3000,
        "data_type": "INTEGER",
        "business_meaning": "Cost of hiring a full-time employee",
        "optimization_role": "Objective coefficient for full-time hiring cost",
        "configuration_type": "scalar_parameter"
      },
      "cost_part_time": {
        "sample_value": 1500,
        "data_type": "INTEGER",
        "business_meaning": "Cost of hiring a part-time employee",
        "optimization_role": "Objective coefficient for part-time hiring cost",
        "configuration_type": "scalar_parameter"
      },
      "total_bonus_budget": {
        "sample_value": 100000,
        "data_type": "INTEGER",
        "business_meaning": "Total budget allocated for employee bonuses",
        "optimization_role": "Constraint bound for total bonus budget",
        "configuration_type": "scalar_parameter"
      }
    },
    "updates_rationale": "These parameters are better suited for configuration logic as they are scalar values used across the optimization model."
  },
  "implemented_optimization_mapping": {
    "objective_coefficients_mapping": {
      "cost_full_time": "business_configuration_logic.cost_full_time",
      "cost_part_time": "business_configuration_logic.cost_part_time"
    },
    "constraint_bounds_mapping": {
      "min_staff[Shop_ID]": "shop_staff_requirements.min_staff",
      "max_full_time[Shop_ID]": "shop_staff_requirements.max_full_time",
      "max_part_time[Shop_ID]": "shop_staff_requirements.max_part_time",
      "total_bonus_budget": "business_configuration_logic.total_bonus_budget"
    },
    "decision_variables_mapping": {
      "x_full_time[Shop_ID]": "shop_staff_requirements.x_full_time",
      "x_part_time[Shop_ID]": "shop_staff_requirements.x_part_time",
      "y[Employee_ID]": "employee_bonus_decisions.y"
    }
  },
  "data_dictionary": {
    "tables": {
      "shop_staff_requirements": {
        "business_purpose": "Staffing requirements for each shop",
        "optimization_role": "constraint_bounds",
        "columns": {
          "shop_id": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for each shop",
            "optimization_purpose": "Index for shop-specific constraints",
            "sample_values": [
              1,
              2,
              3
            ]
          },
          "min_staff": {
            "data_type": "INTEGER",
            "business_meaning": "Minimum staff required for the shop",
            "optimization_purpose": "Lower bound for staffing constraint",
            "sample_values": [
              5,
              6,
              7
            ]
          },
          "max_full_time": {
            "data_type": "INTEGER",
            "business_meaning": "Maximum full-time employees allowed for the shop",
            "optimization_purpose": "Upper bound for full-time hiring constraint",
            "sample_values": [
              10,
              12,
              15
            ]
          },
          "max_part_time": {
            "data_type": "INTEGER",
            "business_meaning": "Maximum part-time employees allowed for the shop",
            "optimization_purpose": "Upper bound for part-time hiring constraint",
            "sample_values": [
              8,
              10,
              12
            ]
          }
        }
      },
      "employee_bonus_decisions": {
        "business_purpose": "Decisions to award bonuses to employees",
        "optimization_role": "decision_variables",
        "columns": {
          "employee_id": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for each employee",
            "optimization_purpose": "Index for bonus decision variable",
            "sample_values": [
              101,
              102,
              103
            ]
          },
          "y": {
            "data_type": "BOOLEAN",
            "business_meaning": "Binary decision to award bonus",
            "optimization_purpose": "Decision variable for bonus allocation",
            "sample_values": [
              true,
              false,
              true
            ]
          }
        }
      }
    }
  },
  "data_mapping": {
    "objective_sources": [
      "business_configuration_logic.cost_full_time",
      "business_configuration_logic.cost_part_time"
    ],
    "constraint_sources": [
      "shop_staff_requirements.min_staff",
      "shop_staff_requirements.max_full_time",
      "shop_staff_requirements.max_part_time",
      "business_configuration_logic.total_bonus_budget"
    ],
    "sample_data_rows": {
      "shop_staff_requirements": 3,
      "employee_bonus_decisions": 3
    }
  },
  "validation": {
    "schema_complete": true,
    "data_consistent": true,
    "math_traceable": true,
    "business_aligned": true,
    "follows_db_standards": true
  }
}
