Iteration 2 - DATA_ENGINEER
Sequence: 4
Timestamp: 2025-07-28 00:04:59

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 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 2):
{
  "database_id": "soccer_2",
  "iteration": 1,
  "business_context": "A soccer league is organizing tryouts for players from various colleges. The goal is to select players for different positions while minimizing the total number of yellow cards among selected players, ensuring that each position is filled and that players are selected from a diverse set of colleges.",
  "optimization_problem_description": "Optimize the selection of players for different positions in a soccer team to minimize the total number of yellow cards among selected players. Ensure that each position is filled and that players are selected from a diverse set of colleges.",
  "optimization_formulation": {
    "objective": "minimize total_yellow_cards = \u2211(yCard[pID] * x[pID])",
    "decision_variables": "x[pID] is a binary variable indicating whether player pID is selected (1) or not (0)",
    "constraints": [
      "\u2211(x[pID] | pPos = position) = required_players for each position",
      "\u2211(x[pID] | cName = college) \u2264 max_players for each college"
    ]
  },
  "current_optimization_to_schema_mapping": {
    "objective_coefficients": {
      "yCard[pID]": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "number of yellow cards for player pID"
      }
    },
    "constraint_bounds": {
      "position_constraints[position]": {
        "currently_mapped_to": "PositionRequirements.required_players",
        "mapping_adequacy": "good",
        "description": "ensures each position is filled with the required number of players"
      },
      "college_constraints[college]": {
        "currently_mapped_to": "CollegeLimits.max_players",
        "mapping_adequacy": "good",
        "description": "ensures diversity by limiting the number of players from each college"
      }
    },
    "decision_variables": {
      "x[pID]": {
        "currently_mapped_to": "Tryout.pID",
        "mapping_adequacy": "good",
        "description": "binary variable indicating if player pID is selected",
        "variable_type": "binary"
      }
    }
  },
  "missing_optimization_requirements": [
    "yCard[pID] data for each player"
  ],
  "iteration_status": {
    "complete": false,
    "confidence": "medium",
    "next_focus": "Incorporate missing yellow card data for players to complete the linear formulation"
  }
}


CURRENT DATABASE SCHEMA:
```sql
-- Iteration 1 Database Schema
-- Objective: Schema adjustments were made to address missing data requirements and improve optimization mapping. Configuration logic was updated to include scalar parameters and formulas.

CREATE TABLE Tryout (
  pID INTEGER,
  pPos STRING,
  cName STRING
);

CREATE TABLE PositionRequirements (
  position STRING,
  required_players INTEGER
);

CREATE TABLE CollegeLimits (
  college STRING,
  max_players INTEGER
);


```


CURRENT DATA DICTIONARY:
{
  "tables": {
    "Tryout": {
      "business_purpose": "Stores tryout data for players",
      "optimization_role": "decision_variables",
      "columns": {
        "pID": {
          "data_type": "INTEGER",
          "business_meaning": "Unique identifier for each player",
          "optimization_purpose": "Used as decision variable index",
          "sample_values": "1, 2, 3"
        },
        "pPos": {
          "data_type": "STRING",
          "business_meaning": "Position the player is trying out for",
          "optimization_purpose": "Used in position constraints",
          "sample_values": "Goalkeeper, Defender, Midfielder, Forward"
        },
        "cName": {
          "data_type": "STRING",
          "business_meaning": "College the player is from",
          "optimization_purpose": "Used in college constraints",
          "sample_values": "College A, College B, College C"
        }
      }
    },
    "PositionRequirements": {
      "business_purpose": "Defines player requirements for each position",
      "optimization_role": "constraint_bounds",
      "columns": {
        "position": {
          "data_type": "STRING",
          "business_meaning": "Position name",
          "optimization_purpose": "Defines position constraints",
          "sample_values": "Goalkeeper, Defender, Midfielder, Forward"
        },
        "required_players": {
          "data_type": "INTEGER",
          "business_meaning": "Number of players required for the position",
          "optimization_purpose": "Ensures each position is filled",
          "sample_values": "1, 4, 4, 2"
        }
      }
    },
    "CollegeLimits": {
      "business_purpose": "Defines limits on player selection from each college",
      "optimization_role": "constraint_bounds",
      "columns": {
        "college": {
          "data_type": "STRING",
          "business_meaning": "College name",
          "optimization_purpose": "Defines college constraints",
          "sample_values": "College A, College B, College C"
        },
        "max_players": {
          "data_type": "INTEGER",
          "business_meaning": "Maximum number of players allowed from the college",
          "optimization_purpose": "Ensures diversity in player selection",
          "sample_values": "3"
        }
      }
    }
  }
}


CURRENT BUSINESS CONFIGURATION LOGIC:
{
  "max_players_per_college": {
    "sample_value": 3,
    "data_type": "INTEGER",
    "business_meaning": "Maximum number of players that can be selected from a single college",
    "optimization_role": "Used to enforce diversity in player selection",
    "configuration_type": "scalar_parameter"
  },
  "players_required_per_position": {
    "formula_expression": "Goalkeeper: 1, Defender: 4, Midfielder: 4, Forward: 2",
    "data_type": "STRING",
    "business_meaning": "Number of players required for each position",
    "optimization_role": "Ensures each position is filled with the required number of players",
    "configuration_type": "business_logic_formula"
  }
}


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

JSON STRUCTURE REQUIRED:

{
  "database_id": "soccer_2",
  "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": "soccer_2",
  "iteration": 2,
  "implementation_summary": "Schema adjustments were made to incorporate missing yellow card data and improve optimization mapping. Configuration logic was updated to include scalar parameters and formulas.",
  "or_requirements_analysis": {
    "mapping_gaps_identified": [
      "yCard[pID] data for each player is missing"
    ],
    "missing_data_requirements": [
      "Yellow card data for each player"
    ],
    "business_configuration_logic_needs": [
      "Scalar parameters like max_players_per_college",
      "Formulas for players_required_per_position"
    ]
  },
  "schema_adjustment_decisions": {
    "tables_to_delete": [],
    "tables_to_create": [
      {
        "table_name": "PlayerYellowCards",
        "purpose": "objective_coefficients",
        "business_meaning": "Stores the number of yellow cards for each player"
      }
    ],
    "tables_to_modify": [
      {
        "table_name": "Tryout",
        "changes": "Add column yCard INTEGER",
        "reason": "To store yellow card data for each player, addressing the missing data requirement"
      }
    ]
  },
  "business_configuration_logic_updates": {
    "configuration_parameters": {
      "max_players_per_college": {
        "sample_value": 3,
        "data_type": "INTEGER",
        "business_meaning": "Maximum number of players that can be selected from a single college",
        "optimization_role": "Used to enforce diversity in player selection",
        "configuration_type": "scalar_parameter"
      },
      "players_required_per_position": {
        "formula_expression": "Goalkeeper: 1, Defender: 4, Midfielder: 4, Forward: 2",
        "data_type": "STRING",
        "business_meaning": "Number of players required for each position",
        "optimization_role": "Ensures each position is filled with the required number of players",
        "configuration_type": "business_logic_formula"
      }
    },
    "updates_rationale": "These parameters are better managed in configuration logic to allow flexibility and easy updates without altering the database schema."
  },
  "implemented_optimization_mapping": {
    "objective_coefficients_mapping": {
      "yCard[pID]": "PlayerYellowCards.yCard"
    },
    "constraint_bounds_mapping": {
      "position_constraints[position]": "PositionRequirements.required_players",
      "college_constraints[college]": "CollegeLimits.max_players"
    },
    "decision_variables_mapping": {
      "x[pID]": "Tryout.pID"
    }
  },
  "data_dictionary": {
    "tables": {
      "Tryout": {
        "business_purpose": "Stores tryout data for players",
        "optimization_role": "decision_variables",
        "columns": {
          "pID": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for each player",
            "optimization_purpose": "Used as decision variable index",
            "sample_values": "1, 2, 3"
          },
          "pPos": {
            "data_type": "STRING",
            "business_meaning": "Position the player is trying out for",
            "optimization_purpose": "Used in position constraints",
            "sample_values": "Goalkeeper, Defender, Midfielder, Forward"
          },
          "cName": {
            "data_type": "STRING",
            "business_meaning": "College the player is from",
            "optimization_purpose": "Used in college constraints",
            "sample_values": "College A, College B, College C"
          },
          "yCard": {
            "data_type": "INTEGER",
            "business_meaning": "Number of yellow cards for the player",
            "optimization_purpose": "Used in the objective function to minimize yellow cards",
            "sample_values": "0, 1, 2"
          }
        }
      },
      "PositionRequirements": {
        "business_purpose": "Defines player requirements for each position",
        "optimization_role": "constraint_bounds",
        "columns": {
          "position": {
            "data_type": "STRING",
            "business_meaning": "Position name",
            "optimization_purpose": "Defines position constraints",
            "sample_values": "Goalkeeper, Defender, Midfielder, Forward"
          },
          "required_players": {
            "data_type": "INTEGER",
            "business_meaning": "Number of players required for the position",
            "optimization_purpose": "Ensures each position is filled",
            "sample_values": "1, 4, 4, 2"
          }
        }
      },
      "CollegeLimits": {
        "business_purpose": "Defines limits on player selection from each college",
        "optimization_role": "constraint_bounds",
        "columns": {
          "college": {
            "data_type": "STRING",
            "business_meaning": "College name",
            "optimization_purpose": "Defines college constraints",
            "sample_values": "College A, College B, College C"
          },
          "max_players": {
            "data_type": "INTEGER",
            "business_meaning": "Maximum number of players allowed from the college",
            "optimization_purpose": "Ensures diversity in player selection",
            "sample_values": "3"
          }
        }
      },
      "PlayerYellowCards": {
        "business_purpose": "Stores the number of yellow cards for each player",
        "optimization_role": "objective_coefficients",
        "columns": {
          "pID": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for each player",
            "optimization_purpose": "Links yellow card data to players",
            "sample_values": "1, 2, 3"
          },
          "yCard": {
            "data_type": "INTEGER",
            "business_meaning": "Number of yellow cards for the player",
            "optimization_purpose": "Used in the objective function to minimize yellow cards",
            "sample_values": "0, 1, 2"
          }
        }
      }
    }
  },
  "data_mapping": {
    "objective_sources": [
      "PlayerYellowCards.yCard"
    ],
    "constraint_sources": [
      "PositionRequirements.required_players",
      "CollegeLimits.max_players"
    ],
    "sample_data_rows": {
      "Tryout": 3,
      "PositionRequirements": 4,
      "CollegeLimits": 3,
      "PlayerYellowCards": 3
    }
  },
  "validation": {
    "schema_complete": true,
    "data_consistent": true,
    "math_traceable": true,
    "business_aligned": true,
    "follows_db_standards": true
  }
}
