Iteration 2 - DATA_ENGINEER
Sequence: 4
Timestamp: 2025-07-25 22:31:07

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 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 2):
{
  "database_id": "dorm_1",
  "iteration": 1,
  "business_context": "Optimize dormitory assignments to minimize the total distance students travel from their home cities to their assigned dorms, while respecting dorm capacity and gender constraints.",
  "optimization_problem_description": "Minimize the total distance traveled by students from their home cities to their assigned dorms, subject to dorm capacity limits and gender matching constraints.",
  "optimization_formulation": {
    "objective": "minimize \u2211(distance[student_id, dorm_id] * assign[student_id, dorm_id])",
    "decision_variables": "assign[student_id, dorm_id] \u2208 {0, 1} (binary variable indicating whether student is assigned to dorm)",
    "constraints": [
      "\u2211(assign[student_id, dorm_id]) = 1 for all student_id (each student is assigned to exactly one dorm)",
      "\u2211(assign[student_id, dorm_id]) \u2264 student_capacity[dorm_id] for all dorm_id (dorm capacity constraint)",
      "assign[student_id, dorm_id] = 0 if gender[student_id] \u2260 gender[dorm_id] (gender matching constraint)"
    ]
  },
  "current_optimization_to_schema_mapping": {
    "objective_coefficients": {
      "distance[student_id, dorm_id]": {
        "currently_mapped_to": "DistanceMatrix.distance",
        "mapping_adequacy": "good",
        "description": "Distance from student's home city to dorm"
      }
    },
    "constraint_bounds": {
      "student_capacity[dorm_id]": {
        "currently_mapped_to": "Dorm.student_capacity",
        "mapping_adequacy": "good",
        "description": "Maximum number of students a dorm can accommodate"
      },
      "gender[student_id]": {
        "currently_mapped_to": "GenderInfo.gender",
        "mapping_adequacy": "good",
        "description": "Gender of student"
      },
      "gender[dorm_id]": {
        "currently_mapped_to": "Dorm.gender",
        "mapping_adequacy": "good",
        "description": "Gender constraint for dorm"
      }
    },
    "decision_variables": {
      "assign[student_id, dorm_id]": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "Binary variable indicating whether student is assigned to dorm",
        "variable_type": "binary"
      }
    }
  },
  "missing_optimization_requirements": [
    "Decision variable assign[student_id, dorm_id] needs to be defined in the schema"
  ],
  "iteration_status": {
    "complete": false,
    "confidence": "medium",
    "next_focus": "Define the decision variable assign[student_id, dorm_id] in the schema"
  }
}


CURRENT DATABASE SCHEMA:
```sql
-- Iteration 1 Database Schema
-- Objective: Schema changes include creating tables for distance matrix and gender information, updating Dorm table for capacity, and adding business configuration logic for scalar parameters and formulas.

CREATE TABLE DistanceMatrix (
  student_id INTEGER,
  dorm_id INTEGER,
  distance FLOAT
);

CREATE TABLE GenderInfo (
  student_id INTEGER,
  dorm_id INTEGER,
  gender STRING
);

CREATE TABLE Dorm (
  dorm_id INTEGER,
  student_capacity INTEGER,
  gender STRING
);


```


CURRENT DATA DICTIONARY:
{
  "tables": {
    "DistanceMatrix": {
      "business_purpose": "Stores distances between student home cities and dorms",
      "optimization_role": "objective_coefficients",
      "columns": {
        "student_id": {
          "data_type": "INTEGER",
          "business_meaning": "Unique identifier for student",
          "optimization_purpose": "Links student to their home city",
          "sample_values": "1, 2, 3"
        },
        "dorm_id": {
          "data_type": "INTEGER",
          "business_meaning": "Unique identifier for dorm",
          "optimization_purpose": "Links dorm to its location",
          "sample_values": "1, 2, 3"
        },
        "distance": {
          "data_type": "FLOAT",
          "business_meaning": "Distance from student's home city to dorm",
          "optimization_purpose": "Used in objective function to minimize total distance",
          "sample_values": "10.5, 15.3, 20.1"
        }
      }
    },
    "GenderInfo": {
      "business_purpose": "Stores gender information for students and dorms",
      "optimization_role": "constraint_bounds",
      "columns": {
        "student_id": {
          "data_type": "INTEGER",
          "business_meaning": "Unique identifier for student",
          "optimization_purpose": "Links student to their gender",
          "sample_values": "1, 2, 3"
        },
        "dorm_id": {
          "data_type": "INTEGER",
          "business_meaning": "Unique identifier for dorm",
          "optimization_purpose": "Links dorm to its gender constraint",
          "sample_values": "1, 2, 3"
        },
        "gender": {
          "data_type": "STRING",
          "business_meaning": "Gender of student or dorm",
          "optimization_purpose": "Ensures gender constraints are respected",
          "sample_values": "Male, Female"
        }
      }
    },
    "Dorm": {
      "business_purpose": "Stores information about dorms",
      "optimization_role": "constraint_bounds",
      "columns": {
        "dorm_id": {
          "data_type": "INTEGER",
          "business_meaning": "Unique identifier for dorm",
          "optimization_purpose": "Links dorm to its capacity and gender",
          "sample_values": "1, 2, 3"
        },
        "student_capacity": {
          "data_type": "INTEGER",
          "business_meaning": "Maximum number of students a dorm can accommodate",
          "optimization_purpose": "Used in capacity constraints",
          "sample_values": "100, 150, 200"
        },
        "gender": {
          "data_type": "STRING",
          "business_meaning": "Gender constraint for dorm",
          "optimization_purpose": "Ensures gender constraints are respected",
          "sample_values": "Male, Female"
        }
      }
    }
  }
}


CURRENT BUSINESS CONFIGURATION LOGIC:
{
  "dorm_capacity_threshold": {
    "sample_value": 100,
    "data_type": "INTEGER",
    "business_meaning": "Maximum number of students a dorm can accommodate",
    "optimization_role": "Used in capacity constraints",
    "configuration_type": "scalar_parameter"
  },
  "distance_calculation_formula": {
    "formula_expression": "sqrt((x2 - x1)^2 + (y2 - y1)^2)",
    "data_type": "STRING",
    "business_meaning": "Calculates Euclidean distance between two points",
    "optimization_role": "Used in objective function to minimize total distance",
    "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": "dorm_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": "dorm_1",
  "iteration": 2,
  "implementation_summary": "Added Assignment table for decision variables, updated data dictionary, and ensured all optimization mappings are complete.",
  "or_requirements_analysis": {
    "mapping_gaps_identified": [
      "Decision variable assign[student_id, dorm_id] is missing in schema"
    ],
    "missing_data_requirements": [
      "Decision variable assign[student_id, dorm_id] needs to be defined in the schema"
    ],
    "business_configuration_logic_needs": [
      "No additional scalar parameters or formulas needed for configuration"
    ]
  },
  "schema_adjustment_decisions": {
    "tables_to_delete": [],
    "tables_to_create": [
      {
        "table_name": "Assignment",
        "purpose": "decision_variables",
        "business_meaning": "Stores binary assignment of students to dorms"
      }
    ],
    "tables_to_modify": []
  },
  "business_configuration_logic_updates": {
    "configuration_parameters": {},
    "updates_rationale": "No additional parameters or formulas needed for configuration"
  },
  "implemented_optimization_mapping": {
    "objective_coefficients_mapping": {
      "distance[student_id, dorm_id]": "DistanceMatrix.distance"
    },
    "constraint_bounds_mapping": {
      "student_capacity[dorm_id]": "Dorm.student_capacity",
      "gender[student_id]": "GenderInfo.gender",
      "gender[dorm_id]": "Dorm.gender"
    },
    "decision_variables_mapping": {
      "assign[student_id, dorm_id]": "Assignment.assign"
    }
  },
  "data_dictionary": {
    "tables": {
      "DistanceMatrix": {
        "business_purpose": "Stores distances between student home cities and dorms",
        "optimization_role": "objective_coefficients",
        "columns": {
          "student_id": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for student",
            "optimization_purpose": "Links student to their home city",
            "sample_values": "1, 2, 3"
          },
          "dorm_id": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for dorm",
            "optimization_purpose": "Links dorm to its location",
            "sample_values": "1, 2, 3"
          },
          "distance": {
            "data_type": "FLOAT",
            "business_meaning": "Distance from student's home city to dorm",
            "optimization_purpose": "Used in objective function to minimize total distance",
            "sample_values": "10.5, 15.3, 20.1"
          }
        }
      },
      "GenderInfo": {
        "business_purpose": "Stores gender information for students and dorms",
        "optimization_role": "constraint_bounds",
        "columns": {
          "student_id": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for student",
            "optimization_purpose": "Links student to their gender",
            "sample_values": "1, 2, 3"
          },
          "dorm_id": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for dorm",
            "optimization_purpose": "Links dorm to its gender constraint",
            "sample_values": "1, 2, 3"
          },
          "gender": {
            "data_type": "STRING",
            "business_meaning": "Gender of student or dorm",
            "optimization_purpose": "Ensures gender constraints are respected",
            "sample_values": "Male, Female"
          }
        }
      },
      "Dorm": {
        "business_purpose": "Stores information about dorms",
        "optimization_role": "constraint_bounds",
        "columns": {
          "dorm_id": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for dorm",
            "optimization_purpose": "Links dorm to its capacity and gender",
            "sample_values": "1, 2, 3"
          },
          "student_capacity": {
            "data_type": "INTEGER",
            "business_meaning": "Maximum number of students a dorm can accommodate",
            "optimization_purpose": "Used in capacity constraints",
            "sample_values": "100, 150, 200"
          },
          "gender": {
            "data_type": "STRING",
            "business_meaning": "Gender constraint for dorm",
            "optimization_purpose": "Ensures gender constraints are respected",
            "sample_values": "Male, Female"
          }
        }
      },
      "Assignment": {
        "business_purpose": "Stores binary assignment of students to dorms",
        "optimization_role": "decision_variables",
        "columns": {
          "student_id": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for student",
            "optimization_purpose": "Links student to their assignment",
            "sample_values": "1, 2, 3"
          },
          "dorm_id": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for dorm",
            "optimization_purpose": "Links dorm to its assignment",
            "sample_values": "1, 2, 3"
          },
          "assign": {
            "data_type": "BOOLEAN",
            "business_meaning": "Binary variable indicating whether student is assigned to dorm",
            "optimization_purpose": "Used in decision variables for optimization",
            "sample_values": "true, false"
          }
        }
      }
    }
  },
  "data_mapping": {
    "objective_sources": [
      "DistanceMatrix.distance"
    ],
    "constraint_sources": [
      "Dorm.student_capacity",
      "GenderInfo.gender",
      "Dorm.gender"
    ],
    "sample_data_rows": {
      "DistanceMatrix": 3,
      "GenderInfo": 3,
      "Dorm": 3,
      "Assignment": 3
    }
  },
  "validation": {
    "schema_complete": true,
    "data_consistent": true,
    "math_traceable": true,
    "business_aligned": true,
    "follows_db_standards": true
  }
}
