Iteration 2 - DATA_ENGINEER
Sequence: 4
Timestamp: 2025-07-25 22:31:02

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": "company_office",
  "iteration": 1,
  "business_context": "A company aims to optimize the allocation of office spaces across multiple buildings to minimize the total leasing cost while ensuring each company's space requirements are met based on their sales and assets.",
  "optimization_problem_description": "The objective is to minimize the total leasing cost of office spaces across different buildings. The decision variables represent the amount of space allocated to each company in each building. Constraints ensure that each company's space requirements are met, the total space in each building is not exceeded, and the allocation is non-negative.",
  "optimization_formulation": {
    "objective": "minimize \u2211(cost_per_sqft[building_id] \u00d7 space_allocated[building_id, company_id])",
    "decision_variables": "space_allocated[building_id, company_id] (continuous)",
    "constraints": [
      "\u2211(space_allocated[building_id, company_id]) \u2265 required_space[company_id] for each company_id",
      "\u2211(space_allocated[building_id, company_id]) \u2264 available_space[building_id] for each building_id",
      "space_allocated[building_id, company_id] \u2265 0 for all building_id, company_id"
    ]
  },
  "current_optimization_to_schema_mapping": {
    "objective_coefficients": {
      "cost_per_sqft[building_id]": {
        "currently_mapped_to": "cost_per_sqft.cost_per_sqft",
        "mapping_adequacy": "good",
        "description": "Cost per square foot for leasing space in each building"
      }
    },
    "constraint_bounds": {
      "required_space[company_id]": {
        "currently_mapped_to": "required_space.required_space",
        "mapping_adequacy": "good",
        "description": "Minimum space required by each company"
      },
      "available_space[building_id]": {
        "currently_mapped_to": "available_space.available_space",
        "mapping_adequacy": "good",
        "description": "Total available space in each building"
      }
    },
    "decision_variables": {
      "space_allocated[building_id, company_id]": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "Amount of space allocated to each company in each building",
        "variable_type": "continuous"
      }
    }
  },
  "missing_optimization_requirements": [
    "space_allocated[building_id, company_id]"
  ],
  "iteration_status": {
    "complete": false,
    "confidence": "medium",
    "next_focus": "Identify and map the decision variable space_allocated[building_id, company_id] to complete the linear optimization formulation."
  }
}


CURRENT DATABASE SCHEMA:
```sql
-- Iteration 1 Database Schema
-- Objective: Schema changes include creating tables for cost_per_sqft, required_space, and available_space. Business configuration logic updated with scalar parameters and formulas for optimization.

CREATE TABLE cost_per_sqft (
  building_id INTEGER,
  cost_per_sqft FLOAT
);

CREATE TABLE required_space (
  company_id INTEGER,
  required_space INTEGER
);

CREATE TABLE available_space (
  building_id INTEGER,
  available_space INTEGER
);


```


CURRENT DATA DICTIONARY:
{
  "tables": {
    "cost_per_sqft": {
      "business_purpose": "Cost per square foot for leasing space in each building",
      "optimization_role": "objective_coefficients",
      "columns": {
        "building_id": {
          "data_type": "INTEGER",
          "business_meaning": "Unique identifier for each building",
          "optimization_purpose": "Index for cost_per_sqft",
          "sample_values": "1, 2, 3"
        },
        "cost_per_sqft": {
          "data_type": "FLOAT",
          "business_meaning": "Cost per square foot for leasing space in the building",
          "optimization_purpose": "Coefficient in the objective function",
          "sample_values": "50.0, 60.0, 70.0"
        }
      }
    },
    "required_space": {
      "business_purpose": "Minimum space required by each company based on sales and assets",
      "optimization_role": "constraint_bounds",
      "columns": {
        "company_id": {
          "data_type": "INTEGER",
          "business_meaning": "Unique identifier for each company",
          "optimization_purpose": "Index for required_space",
          "sample_values": "1, 2, 3"
        },
        "required_space": {
          "data_type": "INTEGER",
          "business_meaning": "Minimum space required by the company",
          "optimization_purpose": "Bound in the constraints",
          "sample_values": "1000, 1500, 2000"
        }
      }
    },
    "available_space": {
      "business_purpose": "Total available space in each building",
      "optimization_role": "constraint_bounds",
      "columns": {
        "building_id": {
          "data_type": "INTEGER",
          "business_meaning": "Unique identifier for each building",
          "optimization_purpose": "Index for available_space",
          "sample_values": "1, 2, 3"
        },
        "available_space": {
          "data_type": "INTEGER",
          "business_meaning": "Total available space in the building",
          "optimization_purpose": "Bound in the constraints",
          "sample_values": "5000, 6000, 7000"
        }
      }
    }
  }
}


CURRENT BUSINESS CONFIGURATION LOGIC:
{
  "cost_per_sqft": {
    "sample_value": 50.0,
    "data_type": "FLOAT",
    "business_meaning": "Cost per square foot for leasing space in each building",
    "optimization_role": "Used in the objective function to minimize total leasing cost",
    "configuration_type": "scalar_parameter"
  },
  "required_space": {
    "sample_value": 1000,
    "data_type": "INTEGER",
    "business_meaning": "Minimum space required by each company based on sales and assets",
    "optimization_role": "Used in constraints to ensure each company's space requirements are met",
    "configuration_type": "scalar_parameter"
  },
  "available_space": {
    "sample_value": 5000,
    "data_type": "INTEGER",
    "business_meaning": "Total available space in each building",
    "optimization_role": "Used in constraints to ensure the total space in each building is not exceeded",
    "configuration_type": "scalar_parameter"
  }
}


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

JSON STRUCTURE REQUIRED:

{
  "database_id": "company_office",
  "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": "company_office",
  "iteration": 2,
  "implementation_summary": "Added space_allocated table to map decision variables, updated business configuration logic with scalar parameters and formulas, and ensured schema follows normalization principles.",
  "or_requirements_analysis": {
    "mapping_gaps_identified": [
      "space_allocated[building_id, company_id] missing in schema"
    ],
    "missing_data_requirements": [
      "space_allocated[building_id, company_id]"
    ],
    "business_configuration_logic_needs": [
      "Scalar parameters for cost_per_sqft, required_space, and available_space",
      "Business logic formulas for optimization constraints"
    ]
  },
  "schema_adjustment_decisions": {
    "tables_to_delete": [],
    "tables_to_create": [
      {
        "table_name": "space_allocated",
        "purpose": "decision_variables",
        "business_meaning": "Amount of space allocated to each company in each building"
      }
    ],
    "tables_to_modify": []
  },
  "business_configuration_logic_updates": {
    "configuration_parameters": {
      "cost_per_sqft": {
        "sample_value": 50.0,
        "data_type": "FLOAT",
        "business_meaning": "Cost per square foot for leasing space in each building",
        "optimization_role": "Used in the objective function to minimize total leasing cost",
        "configuration_type": "scalar_parameter"
      },
      "required_space": {
        "sample_value": 1000,
        "data_type": "INTEGER",
        "business_meaning": "Minimum space required by each company based on sales and assets",
        "optimization_role": "Used in constraints to ensure each company's space requirements are met",
        "configuration_type": "scalar_parameter"
      },
      "available_space": {
        "sample_value": 5000,
        "data_type": "INTEGER",
        "business_meaning": "Total available space in each building",
        "optimization_role": "Used in constraints to ensure the total space in each building is not exceeded",
        "configuration_type": "scalar_parameter"
      },
      "total_leasing_cost_formula": {
        "formula_expression": "sum(cost_per_sqft[building_id] * space_allocated[building_id, company_id])",
        "data_type": "STRING",
        "business_meaning": "Total leasing cost calculation",
        "optimization_role": "Objective function to minimize total leasing cost",
        "configuration_type": "business_logic_formula"
      }
    },
    "updates_rationale": "Scalar parameters and formulas are better suited for configuration logic to maintain flexibility and avoid redundant table structures."
  },
  "implemented_optimization_mapping": {
    "objective_coefficients_mapping": {
      "cost_per_sqft[building_id]": "cost_per_sqft.cost_per_sqft"
    },
    "constraint_bounds_mapping": {
      "required_space[company_id]": "required_space.required_space",
      "available_space[building_id]": "available_space.available_space"
    },
    "decision_variables_mapping": {
      "space_allocated[building_id, company_id]": "space_allocated.space_allocated"
    }
  },
  "data_dictionary": {
    "tables": {
      "cost_per_sqft": {
        "business_purpose": "Cost per square foot for leasing space in each building",
        "optimization_role": "objective_coefficients",
        "columns": {
          "building_id": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for each building",
            "optimization_purpose": "Index for cost_per_sqft",
            "sample_values": "1, 2, 3"
          },
          "cost_per_sqft": {
            "data_type": "FLOAT",
            "business_meaning": "Cost per square foot for leasing space in the building",
            "optimization_purpose": "Coefficient in the objective function",
            "sample_values": "50.0, 60.0, 70.0"
          }
        }
      },
      "required_space": {
        "business_purpose": "Minimum space required by each company based on sales and assets",
        "optimization_role": "constraint_bounds",
        "columns": {
          "company_id": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for each company",
            "optimization_purpose": "Index for required_space",
            "sample_values": "1, 2, 3"
          },
          "required_space": {
            "data_type": "INTEGER",
            "business_meaning": "Minimum space required by the company",
            "optimization_purpose": "Bound in the constraints",
            "sample_values": "1000, 1500, 2000"
          }
        }
      },
      "available_space": {
        "business_purpose": "Total available space in each building",
        "optimization_role": "constraint_bounds",
        "columns": {
          "building_id": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for each building",
            "optimization_purpose": "Index for available_space",
            "sample_values": "1, 2, 3"
          },
          "available_space": {
            "data_type": "INTEGER",
            "business_meaning": "Total available space in the building",
            "optimization_purpose": "Bound in the constraints",
            "sample_values": "5000, 6000, 7000"
          }
        }
      },
      "space_allocated": {
        "business_purpose": "Amount of space allocated to each company in each building",
        "optimization_role": "decision_variables",
        "columns": {
          "building_id": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for each building",
            "optimization_purpose": "Index for space allocation",
            "sample_values": "1, 2, 3"
          },
          "company_id": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for each company",
            "optimization_purpose": "Index for space allocation",
            "sample_values": "1, 2, 3"
          },
          "space_allocated": {
            "data_type": "FLOAT",
            "business_meaning": "Amount of space allocated to the company in the building",
            "optimization_purpose": "Decision variable in optimization",
            "sample_values": "500.0, 600.0, 700.0"
          }
        }
      }
    }
  },
  "data_mapping": {
    "objective_sources": [
      "cost_per_sqft.cost_per_sqft",
      "space_allocated.space_allocated"
    ],
    "constraint_sources": [
      "required_space.required_space",
      "available_space.available_space"
    ],
    "sample_data_rows": {
      "cost_per_sqft": 3,
      "required_space": 3,
      "available_space": 3,
      "space_allocated": 3
    }
  },
  "validation": {
    "schema_complete": true,
    "data_consistent": true,
    "math_traceable": true,
    "business_aligned": true,
    "follows_db_standards": true
  }
}
