Iteration 1 - DATA_ENGINEER
Sequence: 2
Timestamp: 2025-07-25 22:34:08

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 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 1):
{
  "database_id": "loan_1",
  "iteration": 0,
  "business_context": "A bank aims to optimize its loan allocation across branches to maximize total loan disbursement while ensuring that no branch exceeds its customer capacity and that each customer's credit score is respected.",
  "optimization_problem_description": "The bank wants to maximize the total loan amount disbursed across all branches, subject to constraints on branch customer capacity, individual customer credit scores, and the number of loans per customer.",
  "optimization_formulation": {
    "objective": "maximize \u2211(amount[loan_ID])",
    "decision_variables": "amount[loan_ID] (continuous)",
    "constraints": [
      "\u2211(amount[loan_ID] where branch_ID = b) \u2264 no_of_customers[branch_ID] * max_loan_per_customer for each branch b",
      "amount[loan_ID] \u2264 credit_score[cust_ID] * max_loan_per_credit_score for each loan",
      "\u2211(amount[loan_ID] where cust_ID = c) \u2264 max_total_loan_per_customer for each customer c",
      "amount[loan_ID] \u2265 0 for each loan"
    ]
  },
  "current_optimization_to_schema_mapping": {
    "objective_coefficients": {
      "amount[loan_ID]": {
        "currently_mapped_to": "loan.amount",
        "mapping_adequacy": "good",
        "description": "The loan amount to be disbursed"
      }
    },
    "constraint_bounds": {
      "no_of_customers[branch_ID]": {
        "currently_mapped_to": "bank.no_of_customers",
        "mapping_adequacy": "good",
        "description": "The number of customers in a branch"
      },
      "credit_score[cust_ID]": {
        "currently_mapped_to": "customer.credit_score",
        "mapping_adequacy": "good",
        "description": "The credit score of a customer"
      },
      "max_loan_per_customer": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "Maximum loan amount per customer"
      },
      "max_loan_per_credit_score": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "Maximum loan amount per credit score unit"
      },
      "max_total_loan_per_customer": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "Maximum total loan amount per customer"
      }
    },
    "decision_variables": {
      "amount[loan_ID]": {
        "currently_mapped_to": "loan.amount",
        "mapping_adequacy": "good",
        "description": "The loan amount to be disbursed",
        "variable_type": "continuous"
      }
    }
  },
  "missing_optimization_requirements": [
    "max_loan_per_customer",
    "max_loan_per_credit_score",
    "max_total_loan_per_customer"
  ],
  "iteration_status": {
    "complete": false,
    "confidence": "medium",
    "next_focus": "Define and map missing parameters for complete optimization model"
  }
}





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

JSON STRUCTURE REQUIRED:

{
  "database_id": "loan_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": "loan_1",
  "iteration": 1,
  "implementation_summary": "Schema changes include adding missing parameters to business_configuration_logic.json and ensuring all tables meet the 3-row minimum rule. Configuration logic updates address scalar parameters and formulas identified as missing in the OR expert analysis.",
  "or_requirements_analysis": {
    "mapping_gaps_identified": [
      "max_loan_per_customer",
      "max_loan_per_credit_score",
      "max_total_loan_per_customer"
    ],
    "missing_data_requirements": [
      "max_loan_per_customer",
      "max_loan_per_credit_score",
      "max_total_loan_per_customer"
    ],
    "business_configuration_logic_needs": [
      "max_loan_per_customer",
      "max_loan_per_credit_score",
      "max_total_loan_per_customer"
    ]
  },
  "schema_adjustment_decisions": {
    "tables_to_delete": [],
    "tables_to_create": [],
    "tables_to_modify": []
  },
  "business_configuration_logic_updates": {
    "configuration_parameters": {
      "max_loan_per_customer": {
        "sample_value": 10000,
        "data_type": "INTEGER",
        "business_meaning": "Maximum loan amount per customer",
        "optimization_role": "Constraint bound in optimization model",
        "configuration_type": "scalar_parameter"
      },
      "max_loan_per_credit_score": {
        "sample_value": 500,
        "data_type": "INTEGER",
        "business_meaning": "Maximum loan amount per credit score unit",
        "optimization_role": "Constraint bound in optimization model",
        "configuration_type": "scalar_parameter"
      },
      "max_total_loan_per_customer": {
        "sample_value": 50000,
        "data_type": "INTEGER",
        "business_meaning": "Maximum total loan amount per customer",
        "optimization_role": "Constraint bound in optimization model",
        "configuration_type": "scalar_parameter"
      }
    },
    "updates_rationale": "These parameters are scalar values better suited for configuration logic than tables, as they do not require multiple rows and are used directly in constraints."
  },
  "implemented_optimization_mapping": {
    "objective_coefficients_mapping": {
      "amount[loan_ID]": "loan.amount"
    },
    "constraint_bounds_mapping": {
      "no_of_customers[branch_ID]": "bank.no_of_customers",
      "credit_score[cust_ID]": "customer.credit_score",
      "max_loan_per_customer": "business_configuration_logic.max_loan_per_customer",
      "max_loan_per_credit_score": "business_configuration_logic.max_loan_per_credit_score",
      "max_total_loan_per_customer": "business_configuration_logic.max_total_loan_per_customer"
    },
    "decision_variables_mapping": {
      "amount[loan_ID]": "loan.amount"
    }
  },
  "data_dictionary": {
    "tables": {
      "loan": {
        "business_purpose": "Stores loan details for optimization",
        "optimization_role": "decision_variables",
        "columns": {
          "amount": {
            "data_type": "FLOAT",
            "business_meaning": "The loan amount to be disbursed",
            "optimization_purpose": "Decision variable in optimization model",
            "sample_values": "1000.0, 5000.0, 7500.0"
          }
        }
      },
      "bank": {
        "business_purpose": "Stores branch details for optimization",
        "optimization_role": "constraint_bounds",
        "columns": {
          "no_of_customers": {
            "data_type": "INTEGER",
            "business_meaning": "The number of customers in a branch",
            "optimization_purpose": "Constraint bound in optimization model",
            "sample_values": "100, 200, 300"
          }
        }
      },
      "customer": {
        "business_purpose": "Stores customer details for optimization",
        "optimization_role": "constraint_bounds",
        "columns": {
          "credit_score": {
            "data_type": "INTEGER",
            "business_meaning": "The credit score of a customer",
            "optimization_purpose": "Constraint bound in optimization model",
            "sample_values": "700, 750, 800"
          }
        }
      }
    }
  },
  "data_mapping": {
    "objective_sources": [
      "loan.amount"
    ],
    "constraint_sources": [
      "bank.no_of_customers",
      "customer.credit_score",
      "business_configuration_logic.max_loan_per_customer",
      "business_configuration_logic.max_loan_per_credit_score",
      "business_configuration_logic.max_total_loan_per_customer"
    ],
    "sample_data_rows": {
      "loan": 3,
      "bank": 3,
      "customer": 3
    }
  },
  "validation": {
    "schema_complete": true,
    "data_consistent": true,
    "math_traceable": true,
    "business_aligned": true,
    "follows_db_standards": true
  }
}
