Iteration 1 - DATA_ENGINEER
Sequence: 2
Timestamp: 2025-07-27 22:19:49

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": "dorm_1",
  "iteration": 0,
  "business_context": "Assign students to dorms in a way that maximizes the utilization of dorm capacity while respecting gender constraints.",
  "optimization_problem_description": "Optimize the allocation of students to dorms to maximize the utilization of available dorm capacity, ensuring that students are assigned to dorms that match their gender.",
  "optimization_formulation": {
    "objective": "maximize \u2211(1 \u00d7 x[StuID, DormID])",
    "decision_variables": "x[StuID, DormID] - binary variable indicating if student StuID is assigned to dorm DormID",
    "constraints": [
      "\u2211(x[StuID, DormID]) \u2264 Dorm.student_capacity for each DormID",
      "x[StuID, DormID] = 0 if Student.Sex does not match Dorm.gender",
      "\u2211(x[StuID, DormID]) = 1 for each StuID"
    ]
  },
  "current_optimization_to_schema_mapping": {
    "objective_coefficients": {
      "1[StuID, DormID]": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "coefficient representing the assignment of a student to a dorm"
      }
    },
    "constraint_bounds": {
      "Dorm.student_capacity[DormID]": {
        "currently_mapped_to": "Dorm.student_capacity",
        "mapping_adequacy": "good",
        "description": "maximum number of students that can be assigned to a dorm"
      },
      "Student.Sex[StuID]": {
        "currently_mapped_to": "Student.Sex",
        "mapping_adequacy": "good",
        "description": "gender of the student for matching with dorm gender"
      },
      "Dorm.gender[DormID]": {
        "currently_mapped_to": "Dorm.gender",
        "mapping_adequacy": "good",
        "description": "gender restriction of the dorm"
      }
    },
    "decision_variables": {
      "x[StuID, DormID]": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "binary variable indicating if a student is assigned to a dorm",
        "variable_type": "binary"
      }
    }
  },
  "missing_optimization_requirements": [
    "Data on which students are eligible for which dorms based on gender",
    "Binary decision variable setup for student-dorm assignments"
  ],
  "iteration_status": {
    "complete": false,
    "confidence": "medium",
    "next_focus": "Refine decision variable mapping and ensure all necessary data is available for constraints"
  }
}





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

JSON STRUCTURE REQUIRED:

{
  "database_id": "dorm_1",
  "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": "dorm_1",
  "iteration": 1,
  "implementation_summary": "Schema changes include creating new tables for decision variables and objective coefficients, modifying existing tables to fill mapping gaps, and updating configuration logic for scalar parameters and formulas.",
  "or_requirements_analysis": {
    "mapping_gaps_identified": [
      "Objective coefficients mapping is missing",
      "Decision variables mapping is missing"
    ],
    "missing_data_requirements": [
      "Data on student eligibility for dorms based on gender",
      "Binary decision variable setup for student-dorm assignments"
    ],
    "business_configuration_logic_needs": [
      "Dorm capacity limits as scalar parameters",
      "Gender matching logic as a business logic formula"
    ]
  },
  "schema_adjustment_decisions": {
    "tables_to_delete": [],
    "tables_to_create": [
      {
        "table_name": "StudentDormAssignment",
        "purpose": "decision_variables",
        "business_meaning": "Tracks which students are assigned to which dorms"
      },
      {
        "table_name": "ObjectiveCoefficients",
        "purpose": "objective_coefficients",
        "business_meaning": "Stores coefficients for the objective function in student-dorm assignments"
      }
    ],
    "tables_to_modify": [
      {
        "table_name": "Dorm",
        "changes": "Add column for gender restriction",
        "reason": "Ensure dorm gender restrictions are explicitly defined"
      },
      {
        "table_name": "Student",
        "changes": "Ensure gender column is correctly mapped",
        "reason": "Align student gender data with dorm gender restrictions"
      }
    ]
  },
  "business_configuration_logic_updates": {
    "configuration_parameters": {
      "dorm_capacity_limit": {
        "sample_value": "100",
        "data_type": "INTEGER",
        "business_meaning": "Maximum number of students a dorm can accommodate",
        "optimization_role": "Used as a constraint in optimization model",
        "configuration_type": "scalar_parameter"
      },
      "gender_matching_formula": {
        "formula_expression": "IF(Student.Sex = Dorm.gender, 1, 0)",
        "data_type": "STRING",
        "business_meaning": "Ensures students are assigned to dorms matching their gender",
        "optimization_role": "Used as a constraint in optimization model",
        "configuration_type": "business_logic_formula"
      }
    },
    "updates_rationale": "Parameters like dorm capacity and gender matching logic are better managed as configuration logic due to their scalar nature and formulaic expression."
  },
  "implemented_optimization_mapping": {
    "objective_coefficients_mapping": {
      "1[StuID, DormID]": "ObjectiveCoefficients.coefficient"
    },
    "constraint_bounds_mapping": {
      "Dorm.student_capacity[DormID]": "Dorm.student_capacity",
      "Student.Sex[StuID]": "Student.Sex",
      "Dorm.gender[DormID]": "Dorm.gender"
    },
    "decision_variables_mapping": {
      "x[StuID, DormID]": "StudentDormAssignment.assignment"
    }
  },
  "data_dictionary": {
    "tables": {
      "StudentDormAssignment": {
        "business_purpose": "Tracks student assignments to dorms",
        "optimization_role": "decision_variables",
        "columns": {
          "StuID": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for a student",
            "optimization_purpose": "Identifies student in assignment",
            "sample_values": "1, 2, 3"
          },
          "DormID": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for a dorm",
            "optimization_purpose": "Identifies dorm in assignment",
            "sample_values": "101, 102, 103"
          },
          "assignment": {
            "data_type": "BOOLEAN",
            "business_meaning": "Indicates if a student is assigned to a dorm",
            "optimization_purpose": "Binary decision variable",
            "sample_values": "true, false"
          }
        }
      },
      "ObjectiveCoefficients": {
        "business_purpose": "Stores coefficients for objective function",
        "optimization_role": "objective_coefficients",
        "columns": {
          "StuID": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for a student",
            "optimization_purpose": "Part of coefficient index",
            "sample_values": "1, 2, 3"
          },
          "DormID": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for a dorm",
            "optimization_purpose": "Part of coefficient index",
            "sample_values": "101, 102, 103"
          },
          "coefficient": {
            "data_type": "FLOAT",
            "business_meaning": "Coefficient value for objective function",
            "optimization_purpose": "Objective function coefficient",
            "sample_values": "1.0"
          }
        }
      }
    }
  },
  "data_mapping": {
    "objective_sources": [
      "ObjectiveCoefficients.coefficient"
    ],
    "constraint_sources": [
      "Dorm.student_capacity",
      "Student.Sex",
      "Dorm.gender"
    ],
    "sample_data_rows": {
      "StudentDormAssignment": 3,
      "ObjectiveCoefficients": 3
    }
  },
  "validation": {
    "schema_complete": true,
    "data_consistent": true,
    "math_traceable": true,
    "business_aligned": true,
    "follows_db_standards": true
  }
}
