Iteration 2 - DATA_ENGINEER
Sequence: 4
Timestamp: 2025-07-27 22:07:18

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": "allergy_1",
  "iteration": 1,
  "business_context": "A university aims to optimize the assignment of students to dormitories by minimizing the number of students with allergies assigned to non-allergy-friendly dormitories, considering dormitory capacities.",
  "optimization_problem_description": "The university needs to assign students to dormitories such that the number of students with allergies assigned to non-allergy-friendly dormitories is minimized, while respecting dormitory capacities and ensuring each student is assigned to exactly one dormitory.",
  "optimization_formulation": {
    "objective": "minimize sum(penalty_value[student_id, dormitory_id] * x[student_id, dormitory_id])",
    "decision_variables": "x[student_id, dormitory_id] where x is binary, 1 if student is assigned to dormitory, 0 otherwise",
    "constraints": [
      "sum(x[student_id, dormitory_id] for dormitory_id) = 1 for each student_id",
      "sum(x[student_id, dormitory_id] for student_id) <= capacity[dormitory_id] for each dormitory_id",
      "x[student_id, dormitory_id] = 0 if is_allergy_friendly[dormitory_id] = false and penalty_value[student_id, dormitory_id] > 0"
    ]
  },
  "current_optimization_to_schema_mapping": {
    "objective_coefficients": {
      "penalty_value[student_id, dormitory_id]": {
        "currently_mapped_to": "allergy_penalty.penalty_value",
        "mapping_adequacy": "good",
        "description": "Penalty for assigning a student with allergies to a non-allergy-friendly dormitory"
      }
    },
    "constraint_bounds": {
      "capacity[dormitory_id]": {
        "currently_mapped_to": "dormitory_capacity.capacity",
        "mapping_adequacy": "good",
        "description": "Maximum number of students a dormitory can accommodate"
      }
    },
    "decision_variables": {
      "x[student_id, dormitory_id]": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "Binary variable indicating if a student is assigned to a dormitory",
        "variable_type": "binary"
      }
    }
  },
  "missing_optimization_requirements": [
    "Decision variable mapping for x[student_id, dormitory_id]"
  ],
  "iteration_status": {
    "complete": false,
    "confidence": "medium",
    "next_focus": "Map decision variables to schema and ensure all constraints are correctly implemented"
  }
}


CURRENT DATABASE SCHEMA:
```sql
-- Iteration 1 Database Schema
-- Objective: Schema changes include creating tables for dormitory capacities, allergy-friendliness, and penalty values. Configuration logic updated for scalar parameters and formulas.

CREATE TABLE dormitory_capacity (
  dormitory_id INTEGER,
  capacity INTEGER
);

CREATE TABLE dormitory_allergy_friendly (
  dormitory_id INTEGER,
  is_allergy_friendly BOOLEAN
);

CREATE TABLE allergy_penalty (
  student_id INTEGER,
  dormitory_id INTEGER,
  penalty_value FLOAT
);


```


CURRENT DATA DICTIONARY:
{
  "tables": {
    "dormitory_capacity": {
      "business_purpose": "Stores the capacity of each dormitory",
      "optimization_role": "constraint_bounds",
      "columns": {
        "dormitory_id": {
          "data_type": "INTEGER",
          "business_meaning": "Unique identifier for each dormitory",
          "optimization_purpose": "Links capacity to specific dormitory",
          "sample_values": "1, 2, 3"
        },
        "capacity": {
          "data_type": "INTEGER",
          "business_meaning": "Maximum number of students a dormitory can accommodate",
          "optimization_purpose": "Used as a constraint bound",
          "sample_values": "50, 100, 150"
        }
      }
    },
    "dormitory_allergy_friendly": {
      "business_purpose": "Indicates whether each dormitory is allergy-friendly",
      "optimization_role": "business_data",
      "columns": {
        "dormitory_id": {
          "data_type": "INTEGER",
          "business_meaning": "Unique identifier for each dormitory",
          "optimization_purpose": "Links allergy-friendliness to specific dormitory",
          "sample_values": "1, 2, 3"
        },
        "is_allergy_friendly": {
          "data_type": "BOOLEAN",
          "business_meaning": "Indicates if a dormitory is allergy-friendly",
          "optimization_purpose": "Used to determine valid assignments",
          "sample_values": "true, false"
        }
      }
    },
    "allergy_penalty": {
      "business_purpose": "Stores penalty values for assigning students with allergies to non-allergy-friendly dormitories",
      "optimization_role": "objective_coefficients",
      "columns": {
        "student_id": {
          "data_type": "INTEGER",
          "business_meaning": "Unique identifier for each student",
          "optimization_purpose": "Links penalty to specific student",
          "sample_values": "101, 102, 103"
        },
        "dormitory_id": {
          "data_type": "INTEGER",
          "business_meaning": "Unique identifier for each dormitory",
          "optimization_purpose": "Links penalty to specific dormitory",
          "sample_values": "1, 2, 3"
        },
        "penalty_value": {
          "data_type": "FLOAT",
          "business_meaning": "Penalty for assigning a student with allergies to a non-allergy-friendly dormitory",
          "optimization_purpose": "Used in objective function",
          "sample_values": "10.0, 20.0, 30.0"
        }
      }
    }
  }
}


CURRENT BUSINESS CONFIGURATION LOGIC:
{
  "dormitory_capacity_limit": {
    "sample_value": "100",
    "data_type": "INTEGER",
    "business_meaning": "Maximum number of students a dormitory can accommodate",
    "optimization_role": "Used as a constraint bound in optimization model",
    "configuration_type": "scalar_parameter"
  },
  "allergy_penalty_formula": {
    "formula_expression": "penalty_value * number_of_students_with_allergies",
    "data_type": "STRING",
    "business_meaning": "Calculates penalty for assigning students with allergies to non-allergy-friendly dormitories",
    "optimization_role": "Used in objective function to minimize penalties",
    "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": "allergy_1",
  "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": "allergy_1",
  "iteration": 2,
  "implementation_summary": "Schema changes include creating a table for decision variables and updating configuration logic for scalar parameters and formulas based on OR expert mapping analysis.",
  "or_requirements_analysis": {
    "mapping_gaps_identified": [
      "Decision variable mapping for x[student_id, dormitory_id] is missing."
    ],
    "missing_data_requirements": [
      "Mapping for decision variables x[student_id, dormitory_id]."
    ],
    "business_configuration_logic_needs": [
      "Dormitory capacity limit as a scalar parameter.",
      "Allergy penalty calculation as a business logic formula."
    ]
  },
  "schema_adjustment_decisions": {
    "tables_to_delete": [],
    "tables_to_create": [
      {
        "table_name": "student_dormitory_assignment",
        "purpose": "decision_variables",
        "business_meaning": "Stores the assignment of students to dormitories as binary decision variables."
      }
    ],
    "tables_to_modify": []
  },
  "business_configuration_logic_updates": {
    "configuration_parameters": {
      "dormitory_capacity_limit": {
        "sample_value": "100",
        "data_type": "INTEGER",
        "business_meaning": "Maximum number of students a dormitory can accommodate",
        "optimization_role": "Used as a constraint bound in optimization model",
        "configuration_type": "scalar_parameter"
      },
      "allergy_penalty_formula": {
        "formula_expression": "penalty_value * number_of_students_with_allergies",
        "data_type": "STRING",
        "business_meaning": "Calculates penalty for assigning students with allergies to non-allergy-friendly dormitories",
        "optimization_role": "Used in objective function to minimize penalties",
        "configuration_type": "business_logic_formula"
      }
    },
    "updates_rationale": "These parameters are better in configuration logic than tables because they represent scalar values and formulas that do not require tabular data storage."
  },
  "implemented_optimization_mapping": {
    "objective_coefficients_mapping": {
      "penalty_value[student_id, dormitory_id]": "allergy_penalty.penalty_value"
    },
    "constraint_bounds_mapping": {
      "capacity[dormitory_id]": "dormitory_capacity.capacity"
    },
    "decision_variables_mapping": {
      "x[student_id, dormitory_id]": "student_dormitory_assignment.assignment"
    }
  },
  "data_dictionary": {
    "tables": {
      "dormitory_capacity": {
        "business_purpose": "Stores the capacity of each dormitory",
        "optimization_role": "constraint_bounds",
        "columns": {
          "dormitory_id": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for each dormitory",
            "optimization_purpose": "Links capacity to specific dormitory",
            "sample_values": "1, 2, 3"
          },
          "capacity": {
            "data_type": "INTEGER",
            "business_meaning": "Maximum number of students a dormitory can accommodate",
            "optimization_purpose": "Used as a constraint bound",
            "sample_values": "50, 100, 150"
          }
        }
      },
      "dormitory_allergy_friendly": {
        "business_purpose": "Indicates whether each dormitory is allergy-friendly",
        "optimization_role": "business_data",
        "columns": {
          "dormitory_id": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for each dormitory",
            "optimization_purpose": "Links allergy-friendliness to specific dormitory",
            "sample_values": "1, 2, 3"
          },
          "is_allergy_friendly": {
            "data_type": "BOOLEAN",
            "business_meaning": "Indicates if a dormitory is allergy-friendly",
            "optimization_purpose": "Used to determine valid assignments",
            "sample_values": "true, false"
          }
        }
      },
      "allergy_penalty": {
        "business_purpose": "Stores penalty values for assigning students with allergies to non-allergy-friendly dormitories",
        "optimization_role": "objective_coefficients",
        "columns": {
          "student_id": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for each student",
            "optimization_purpose": "Links penalty to specific student",
            "sample_values": "101, 102, 103"
          },
          "dormitory_id": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for each dormitory",
            "optimization_purpose": "Links penalty to specific dormitory",
            "sample_values": "1, 2, 3"
          },
          "penalty_value": {
            "data_type": "FLOAT",
            "business_meaning": "Penalty for assigning a student with allergies to a non-allergy-friendly dormitory",
            "optimization_purpose": "Used in objective function",
            "sample_values": "10.0, 20.0, 30.0"
          }
        }
      },
      "student_dormitory_assignment": {
        "business_purpose": "Stores the assignment of students to dormitories as binary decision variables",
        "optimization_role": "decision_variables",
        "columns": {
          "student_id": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for each student",
            "optimization_purpose": "Links assignment to specific student",
            "sample_values": "101, 102, 103"
          },
          "dormitory_id": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for each dormitory",
            "optimization_purpose": "Links assignment to specific dormitory",
            "sample_values": "1, 2, 3"
          },
          "assignment": {
            "data_type": "BOOLEAN",
            "business_meaning": "Binary variable indicating if a student is assigned to a dormitory",
            "optimization_purpose": "Used as a decision variable in optimization",
            "sample_values": "true, false"
          }
        }
      }
    }
  },
  "data_mapping": {
    "objective_sources": [
      "allergy_penalty.penalty_value"
    ],
    "constraint_sources": [
      "dormitory_capacity.capacity",
      "student_dormitory_assignment.assignment"
    ],
    "sample_data_rows": {
      "dormitory_capacity": 3,
      "dormitory_allergy_friendly": 3,
      "allergy_penalty": 3,
      "student_dormitory_assignment": 3
    }
  },
  "validation": {
    "schema_complete": true,
    "data_consistent": true,
    "math_traceable": true,
    "business_aligned": true,
    "follows_db_standards": true
  }
}
