Iteration 2 - DATA_ENGINEER
Sequence: 4
Timestamp: 2025-07-27 22:26:58

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": "school_bus",
  "iteration": 1,
  "business_context": "Optimize the allocation of drivers to schools to minimize the total years of experience mismatch while ensuring all schools have the required number of drivers and only full-time drivers are assigned.",
  "optimization_problem_description": "The goal is to assign drivers to schools such that the total mismatch in years of experience is minimized. Each school requires a certain number of drivers, and each driver can be assigned to one school. The assignment should respect the full-time status of drivers.",
  "optimization_formulation": {
    "objective": "minimize total_experience_mismatch = sum(Years_Working[i][j] * x[i][j] for all i, j)",
    "decision_variables": "x[i][j] is a binary variable indicating if driver i is assigned to school j",
    "constraints": [
      "sum(x[i][j] for all j) <= 1 for all i (each driver can be assigned to at most one school)",
      "sum(x[i][j] for all i) = RequiredDrivers[j] for all j (each school must have the required number of drivers)",
      "x[i][j] <= IsFullTime[i] for all i, j (only full-time drivers can be assigned)"
    ]
  },
  "current_optimization_to_schema_mapping": {
    "objective_coefficients": {
      "Years_Working[i][j]": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "Years of experience mismatch between driver i and school j"
      }
    },
    "constraint_bounds": {
      "RequiredDrivers[j]": {
        "currently_mapped_to": "SchoolRequirements.RequiredDrivers",
        "mapping_adequacy": "good",
        "description": "Number of drivers required by school j"
      }
    },
    "decision_variables": {
      "x[i][j]": {
        "currently_mapped_to": "DriverAssignments.Assigned",
        "mapping_adequacy": "good",
        "description": "Binary decision variable indicating if driver i is assigned to school j",
        "variable_type": "binary"
      }
    }
  },
  "missing_optimization_requirements": [
    "Years_Working[i][j]"
  ],
  "iteration_status": {
    "complete": false,
    "confidence": "medium",
    "next_focus": "Incorporate missing data on years of experience mismatch"
  }
}


CURRENT DATABASE SCHEMA:
```sql
-- Iteration 1 Database Schema
-- Objective: Schema changes include creating new tables for missing data requirements and updating existing tables to fill mapping gaps. Business configuration logic is updated to handle scalar parameters and formulas.

CREATE TABLE DriverAssignments (
  DriverID INTEGER,
  SchoolID INTEGER,
  Assigned BOOLEAN
);

CREATE TABLE SchoolRequirements (
  SchoolID INTEGER,
  RequiredDrivers INTEGER
);

CREATE TABLE Drivers (
  DriverID INTEGER,
  IsFullTime BOOLEAN
);


```


CURRENT DATA DICTIONARY:
{
  "tables": {
    "DriverAssignments": {
      "business_purpose": "Tracks driver assignments to schools",
      "optimization_role": "decision_variables",
      "columns": {
        "DriverID": {
          "data_type": "INTEGER",
          "business_meaning": "Unique identifier for each driver",
          "optimization_purpose": "Identifies driver in assignment decision variable",
          "sample_values": "1, 2, 3"
        },
        "SchoolID": {
          "data_type": "INTEGER",
          "business_meaning": "Unique identifier for each school",
          "optimization_purpose": "Identifies school in assignment decision variable",
          "sample_values": "101, 102, 103"
        },
        "Assigned": {
          "data_type": "BOOLEAN",
          "business_meaning": "Indicates if a driver is assigned to a school",
          "optimization_purpose": "Binary decision variable for driver assignment",
          "sample_values": "true, false"
        }
      }
    },
    "SchoolRequirements": {
      "business_purpose": "Stores driver requirements for each school",
      "optimization_role": "constraint_bounds",
      "columns": {
        "SchoolID": {
          "data_type": "INTEGER",
          "business_meaning": "Unique identifier for each school",
          "optimization_purpose": "Identifies school in driver requirement constraint",
          "sample_values": "101, 102, 103"
        },
        "RequiredDrivers": {
          "data_type": "INTEGER",
          "business_meaning": "Number of drivers required by the school",
          "optimization_purpose": "Constraint bound for driver assignment",
          "sample_values": "3, 4, 5"
        }
      }
    },
    "Drivers": {
      "business_purpose": "Stores information about drivers",
      "optimization_role": "business_data",
      "columns": {
        "DriverID": {
          "data_type": "INTEGER",
          "business_meaning": "Unique identifier for each driver",
          "optimization_purpose": "Identifies driver in optimization model",
          "sample_values": "1, 2, 3"
        },
        "IsFullTime": {
          "data_type": "BOOLEAN",
          "business_meaning": "Indicates if a driver is full-time",
          "optimization_purpose": "Used in constraint to ensure only full-time drivers are assigned",
          "sample_values": "true, false"
        }
      }
    }
  }
}


CURRENT BUSINESS CONFIGURATION LOGIC:
{
  "required_drivers": {
    "sample_value": "3",
    "data_type": "INTEGER",
    "business_meaning": "Number of drivers required by each school",
    "optimization_role": "Used in constraint to ensure each school gets required drivers",
    "configuration_type": "scalar_parameter"
  },
  "is_full_time": {
    "sample_value": "true",
    "data_type": "BOOLEAN",
    "business_meaning": "Indicates if a driver is full-time",
    "optimization_role": "Used in constraint to ensure only full-time drivers are assigned",
    "configuration_type": "scalar_parameter"
  }
}


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

JSON STRUCTURE REQUIRED:

{
  "database_id": "school_bus",
  "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": "school_bus",
  "iteration": 2,
  "implementation_summary": "Schema changes include creating a new table for years of experience mismatch and updating existing tables to fill mapping gaps. Business configuration logic is updated to handle scalar parameters and formulas.",
  "or_requirements_analysis": {
    "mapping_gaps_identified": [
      "Years_Working[i][j] is missing from the schema"
    ],
    "missing_data_requirements": [
      "Years_Working[i][j]"
    ],
    "business_configuration_logic_needs": [
      "Scalar parameters like required_drivers and is_full_time are better suited for configuration than tables"
    ]
  },
  "schema_adjustment_decisions": {
    "tables_to_delete": [],
    "tables_to_create": [
      {
        "table_name": "ExperienceMismatch",
        "purpose": "objective_coefficients",
        "business_meaning": "Stores years of experience mismatch between drivers and schools"
      }
    ],
    "tables_to_modify": []
  },
  "business_configuration_logic_updates": {
    "configuration_parameters": {
      "required_drivers": {
        "sample_value": "3",
        "data_type": "INTEGER",
        "business_meaning": "Number of drivers required by each school",
        "optimization_role": "Used in constraint to ensure each school gets required drivers",
        "configuration_type": "scalar_parameter"
      },
      "is_full_time": {
        "sample_value": "true",
        "data_type": "BOOLEAN",
        "business_meaning": "Indicates if a driver is full-time",
        "optimization_role": "Used in constraint to ensure only full-time drivers are assigned",
        "configuration_type": "scalar_parameter"
      }
    },
    "updates_rationale": "These parameters are better in configuration logic than tables because they are scalar values that do not require tabular representation."
  },
  "implemented_optimization_mapping": {
    "objective_coefficients_mapping": {
      "Years_Working[i][j]": "ExperienceMismatch.YearsMismatch"
    },
    "constraint_bounds_mapping": {
      "RequiredDrivers[j]": "SchoolRequirements.RequiredDrivers"
    },
    "decision_variables_mapping": {
      "x[i][j]": "DriverAssignments.Assigned"
    }
  },
  "data_dictionary": {
    "tables": {
      "DriverAssignments": {
        "business_purpose": "Tracks driver assignments to schools",
        "optimization_role": "decision_variables",
        "columns": {
          "DriverID": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for each driver",
            "optimization_purpose": "Identifies driver in assignment decision variable",
            "sample_values": "1, 2, 3"
          },
          "SchoolID": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for each school",
            "optimization_purpose": "Identifies school in assignment decision variable",
            "sample_values": "101, 102, 103"
          },
          "Assigned": {
            "data_type": "BOOLEAN",
            "business_meaning": "Indicates if a driver is assigned to a school",
            "optimization_purpose": "Binary decision variable for driver assignment",
            "sample_values": "true, false"
          }
        }
      },
      "SchoolRequirements": {
        "business_purpose": "Stores driver requirements for each school",
        "optimization_role": "constraint_bounds",
        "columns": {
          "SchoolID": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for each school",
            "optimization_purpose": "Identifies school in driver requirement constraint",
            "sample_values": "101, 102, 103"
          },
          "RequiredDrivers": {
            "data_type": "INTEGER",
            "business_meaning": "Number of drivers required by the school",
            "optimization_purpose": "Constraint bound for driver assignment",
            "sample_values": "3, 4, 5"
          }
        }
      },
      "Drivers": {
        "business_purpose": "Stores information about drivers",
        "optimization_role": "business_data",
        "columns": {
          "DriverID": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for each driver",
            "optimization_purpose": "Identifies driver in optimization model",
            "sample_values": "1, 2, 3"
          },
          "IsFullTime": {
            "data_type": "BOOLEAN",
            "business_meaning": "Indicates if a driver is full-time",
            "optimization_purpose": "Used in constraint to ensure only full-time drivers are assigned",
            "sample_values": "true, false"
          }
        }
      },
      "ExperienceMismatch": {
        "business_purpose": "Stores years of experience mismatch between drivers and schools",
        "optimization_role": "objective_coefficients",
        "columns": {
          "DriverID": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for each driver",
            "optimization_purpose": "Identifies driver in experience mismatch calculation",
            "sample_values": "1, 2, 3"
          },
          "SchoolID": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for each school",
            "optimization_purpose": "Identifies school in experience mismatch calculation",
            "sample_values": "101, 102, 103"
          },
          "YearsMismatch": {
            "data_type": "INTEGER",
            "business_meaning": "Years of experience mismatch between driver and school",
            "optimization_purpose": "Coefficient in objective function to minimize experience mismatch",
            "sample_values": "0, 1, 2"
          }
        }
      }
    }
  },
  "data_mapping": {
    "objective_sources": [
      "ExperienceMismatch.YearsMismatch"
    ],
    "constraint_sources": [
      "SchoolRequirements.RequiredDrivers",
      "Drivers.IsFullTime"
    ],
    "sample_data_rows": {
      "DriverAssignments": 3,
      "SchoolRequirements": 3,
      "Drivers": 3,
      "ExperienceMismatch": 3
    }
  },
  "validation": {
    "schema_complete": true,
    "data_consistent": true,
    "math_traceable": true,
    "business_aligned": true,
    "follows_db_standards": true
  }
}
