Iteration 1 - DATA_ENGINEER
Sequence: 2
Timestamp: 2025-07-27 23:27:52

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": "game_injury",
  "iteration": 0,
  "business_context": "A sports league wants to optimize the scheduling of games to maximize attendance while minimizing the risk of player injuries. The league aims to balance the number of home games played at each stadium with the average attendance and the number of injuries reported.",
  "optimization_problem_description": "The objective is to maximize the total attendance across all games while ensuring that the number of home games at each stadium does not exceed its capacity and minimizing the number of injuries. The decision variables include the number of games scheduled at each stadium and the allocation of games to minimize injuries.",
  "optimization_formulation": {
    "objective": "maximize total_attendance = \u2211(Average_Attendance[stadium_id] \u00d7 Home_Games[stadium_id]) - \u2211(Injury_Risk[game_id] \u00d7 Number_of_matches[injury_id])",
    "decision_variables": "Home_Games[stadium_id] (integer), Game_Schedule[game_id] (binary)",
    "constraints": [
      "\u2211(Home_Games[stadium_id]) \u2264 Total_Home_Games",
      "Home_Games[stadium_id] \u2264 Capacity_Percentage[stadium_id] \u00d7 Total_Attendance[stadium_id]",
      "\u2211(Game_Schedule[game_id]) = 1 for each game_id",
      "Game_Schedule[game_id] \u00d7 Injury_Risk[game_id] \u2264 Max_Injury_Risk"
    ]
  },
  "current_optimization_to_schema_mapping": {
    "objective_coefficients": {
      "Average_Attendance[stadium_id]": {
        "currently_mapped_to": "stadium.Average_Attendance",
        "mapping_adequacy": "good",
        "description": "Represents the average attendance expected at each stadium"
      },
      "Injury_Risk[game_id]": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "Represents the risk of injury associated with each game"
      }
    },
    "constraint_bounds": {
      "Total_Home_Games": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "Represents the total number of home games that can be scheduled"
      },
      "Capacity_Percentage[stadium_id]": {
        "currently_mapped_to": "stadium.Capacity_Percentage",
        "mapping_adequacy": "good",
        "description": "Represents the maximum capacity utilization allowed for each stadium"
      },
      "Max_Injury_Risk": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "Represents the maximum allowable injury risk for scheduling"
      }
    },
    "decision_variables": {
      "Home_Games[stadium_id]": {
        "currently_mapped_to": "stadium.Home_Games",
        "mapping_adequacy": "good",
        "description": "Number of home games scheduled at each stadium",
        "variable_type": "integer"
      },
      "Game_Schedule[game_id]": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "Binary variable indicating if a game is scheduled",
        "variable_type": "binary"
      }
    }
  },
  "missing_optimization_requirements": [
    "Injury_Risk data for each game",
    "Total_Home_Games parameter",
    "Max_Injury_Risk parameter"
  ],
  "iteration_status": {
    "complete": false,
    "confidence": "medium",
    "next_focus": "Refine the mapping of injury risk and scheduling constraints"
  }
}





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

JSON STRUCTURE REQUIRED:

{
  "database_id": "game_injury",
  "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": "game_injury",
  "iteration": 1,
  "implementation_summary": "Schema changes include adding missing tables for injury risk and game schedule, and updating configuration logic for scalar parameters and formulas.",
  "or_requirements_analysis": {
    "mapping_gaps_identified": [
      "Injury_Risk[game_id] missing",
      "Total_Home_Games missing",
      "Max_Injury_Risk missing",
      "Game_Schedule[game_id] missing"
    ],
    "missing_data_requirements": [
      "Injury_Risk data for each game",
      "Total_Home_Games parameter",
      "Max_Injury_Risk parameter"
    ],
    "business_configuration_logic_needs": [
      "Total_Home_Games",
      "Max_Injury_Risk"
    ]
  },
  "schema_adjustment_decisions": {
    "tables_to_delete": [],
    "tables_to_create": [
      {
        "table_name": "injury_risk",
        "purpose": "objective_coefficients",
        "business_meaning": "Represents the risk of injury associated with each game"
      },
      {
        "table_name": "game_schedule",
        "purpose": "decision_variables",
        "business_meaning": "Binary variable indicating if a game is scheduled"
      }
    ],
    "tables_to_modify": [
      {
        "table_name": "stadium",
        "changes": "Add column for Capacity_Percentage",
        "reason": "To ensure mapping adequacy for capacity constraints"
      }
    ]
  },
  "business_configuration_logic_updates": {
    "configuration_parameters": {
      "Total_Home_Games": {
        "sample_value": "20",
        "data_type": "INTEGER",
        "business_meaning": "Represents the total number of home games that can be scheduled",
        "optimization_role": "Used as a constraint bound in optimization model",
        "configuration_type": "scalar_parameter"
      },
      "Max_Injury_Risk": {
        "sample_value": "0.05",
        "data_type": "FLOAT",
        "business_meaning": "Represents the maximum allowable injury risk for scheduling",
        "optimization_role": "Used as a constraint bound in optimization model",
        "configuration_type": "scalar_parameter"
      }
    },
    "updates_rationale": "These parameters are better suited for configuration logic as they are scalar values used in constraints."
  },
  "implemented_optimization_mapping": {
    "objective_coefficients_mapping": {
      "Average_Attendance[stadium_id]": "stadium.Average_Attendance",
      "Injury_Risk[game_id]": "injury_risk.Risk"
    },
    "constraint_bounds_mapping": {
      "Total_Home_Games": "business_configuration_logic.Total_Home_Games",
      "Capacity_Percentage[stadium_id]": "stadium.Capacity_Percentage",
      "Max_Injury_Risk": "business_configuration_logic.Max_Injury_Risk"
    },
    "decision_variables_mapping": {
      "Home_Games[stadium_id]": "stadium.Home_Games",
      "Game_Schedule[game_id]": "game_schedule.Scheduled"
    }
  },
  "data_dictionary": {
    "tables": {
      "stadium": {
        "business_purpose": "Stores information about each stadium including capacity and average attendance",
        "optimization_role": "decision_variables/objective_coefficients",
        "columns": {
          "Average_Attendance": {
            "data_type": "INTEGER",
            "business_meaning": "Average attendance expected at each stadium",
            "optimization_purpose": "Used in objective function to calculate total attendance",
            "sample_values": "5000, 10000, 15000"
          },
          "Capacity_Percentage": {
            "data_type": "FLOAT",
            "business_meaning": "Maximum capacity utilization allowed for each stadium",
            "optimization_purpose": "Used in constraints to limit home games",
            "sample_values": "0.8, 0.9, 1.0"
          },
          "Home_Games": {
            "data_type": "INTEGER",
            "business_meaning": "Number of home games scheduled at each stadium",
            "optimization_purpose": "Decision variable for scheduling",
            "sample_values": "5, 10, 15"
          }
        }
      },
      "injury_risk": {
        "business_purpose": "Stores injury risk associated with each game",
        "optimization_role": "objective_coefficients",
        "columns": {
          "Risk": {
            "data_type": "FLOAT",
            "business_meaning": "Risk of injury for each game",
            "optimization_purpose": "Used in objective function to minimize injuries",
            "sample_values": "0.01, 0.02, 0.03"
          }
        }
      },
      "game_schedule": {
        "business_purpose": "Indicates if a game is scheduled",
        "optimization_role": "decision_variables",
        "columns": {
          "Scheduled": {
            "data_type": "BOOLEAN",
            "business_meaning": "Binary variable indicating if a game is scheduled",
            "optimization_purpose": "Decision variable for game scheduling",
            "sample_values": "true, false"
          }
        }
      }
    }
  },
  "data_mapping": {
    "objective_sources": [
      "stadium.Average_Attendance",
      "injury_risk.Risk"
    ],
    "constraint_sources": [
      "stadium.Capacity_Percentage",
      "business_configuration_logic.Total_Home_Games",
      "business_configuration_logic.Max_Injury_Risk"
    ],
    "sample_data_rows": {
      "stadium": 3,
      "injury_risk": 5,
      "game_schedule": 5
    }
  },
  "validation": {
    "schema_complete": true,
    "data_consistent": true,
    "math_traceable": true,
    "business_aligned": true,
    "follows_db_standards": true
  }
}
