Iteration 1 - DATA_ENGINEER
Sequence: 2
Timestamp: 2025-07-28 00:19:24

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": "wedding",
  "iteration": 0,
  "business_context": "Optimize the allocation of weddings to churches to maximize the number of weddings held while respecting church availability and gender balance.",
  "optimization_problem_description": "The goal is to maximize the number of weddings held in available churches, ensuring that each wedding has one male and one female participant, and that each church can host a limited number of weddings per year.",
  "optimization_formulation": {
    "objective": "maximize total_weddings = \u2211(wedding_allocation[church_id, male_id, female_id])",
    "decision_variables": "wedding_allocation[church_id, male_id, female_id] (binary: 1 if wedding is held, 0 otherwise)",
    "constraints": [
      "\u2211(wedding_allocation[church_id, male_id, female_id]) \u2264 church_capacity[church_id] for each church_id",
      "\u2211(wedding_allocation[church_id, male_id, female_id]) \u2264 1 for each male_id",
      "\u2211(wedding_allocation[church_id, male_id, female_id]) \u2264 1 for each female_id",
      "wedding_allocation[church_id, male_id, female_id] \u2208 {0, 1}"
    ]
  },
  "current_optimization_to_schema_mapping": {
    "objective_coefficients": {
      "total_weddings": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "represents the total number of weddings held"
      }
    },
    "constraint_bounds": {
      "church_capacity[church_id]": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "maximum number of weddings a church can host per year"
      }
    },
    "decision_variables": {
      "wedding_allocation[church_id, male_id, female_id]": {
        "currently_mapped_to": "wedding.Church_ID, wedding.Male_ID, wedding.Female_ID",
        "mapping_adequacy": "partial",
        "description": "binary variable indicating if a wedding is held at a specific church with specific male and female participants",
        "variable_type": "binary"
      }
    }
  },
  "missing_optimization_requirements": [
    "Church capacity data to define church_capacity[church_id]",
    "Data to ensure each male and female participates in at most one wedding"
  ],
  "iteration_status": {
    "complete": false,
    "confidence": "medium",
    "next_focus": "Gather missing data on church capacities and ensure unique participation of individuals in weddings"
  }
}





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

JSON STRUCTURE REQUIRED:

{
  "database_id": "wedding",
  "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": "wedding",
  "iteration": 1,
  "implementation_summary": "Schema changes include creating new tables for church capacities and participant constraints, modifying existing tables to improve mapping adequacy, and updating configuration logic for scalar parameters and formulas.",
  "or_requirements_analysis": {
    "mapping_gaps_identified": [
      "total_weddings mapping missing",
      "church_capacity[church_id] mapping missing"
    ],
    "missing_data_requirements": [
      "Church capacity data",
      "Data to ensure unique participation of males and females in weddings"
    ],
    "business_configuration_logic_needs": [
      "Church capacity as scalar parameter",
      "Unique participation constraints as business logic formulas"
    ]
  },
  "schema_adjustment_decisions": {
    "tables_to_delete": [],
    "tables_to_create": [
      {
        "table_name": "ChurchCapacity",
        "purpose": "constraint_bounds",
        "business_meaning": "Stores the maximum number of weddings each church can host per year"
      },
      {
        "table_name": "ParticipantConstraints",
        "purpose": "constraint_bounds",
        "business_meaning": "Ensures each male and female participates in at most one wedding"
      }
    ],
    "tables_to_modify": [
      {
        "table_name": "wedding",
        "changes": "Add columns for total_weddings and improve mapping for decision variables",
        "reason": "Addresses missing mapping for total_weddings and improves decision variable mapping"
      }
    ]
  },
  "business_configuration_logic_updates": {
    "configuration_parameters": {
      "church_capacity": {
        "sample_value": "100",
        "data_type": "INTEGER",
        "business_meaning": "Maximum number of weddings a church can host per year",
        "optimization_role": "Used as a constraint bound in optimization model",
        "configuration_type": "scalar_parameter"
      },
      "unique_participation_formula": {
        "formula_expression": "SUM(wedding_allocation[church_id, male_id, female_id]) <= 1",
        "data_type": "STRING",
        "business_meaning": "Ensures each participant is involved in at most one wedding",
        "optimization_role": "Used as a constraint in optimization model",
        "configuration_type": "business_logic_formula"
      }
    },
    "updates_rationale": "Parameters like church capacity and participation constraints are better managed as configuration logic due to their scalar nature and formulaic expressions."
  },
  "implemented_optimization_mapping": {
    "objective_coefficients_mapping": {
      "total_weddings": "wedding.total_weddings"
    },
    "constraint_bounds_mapping": {
      "church_capacity[church_id]": "ChurchCapacity.capacity"
    },
    "decision_variables_mapping": {
      "wedding_allocation[church_id, male_id, female_id]": "wedding.Church_ID, wedding.Male_ID, wedding.Female_ID"
    }
  },
  "data_dictionary": {
    "tables": {
      "wedding": {
        "business_purpose": "Stores information about weddings held at churches",
        "optimization_role": "decision_variables",
        "columns": {
          "Church_ID": {
            "data_type": "INTEGER",
            "business_meaning": "Identifier for the church",
            "optimization_purpose": "Used to allocate weddings to churches",
            "sample_values": "1, 2, 3"
          },
          "Male_ID": {
            "data_type": "INTEGER",
            "business_meaning": "Identifier for the male participant",
            "optimization_purpose": "Used to ensure unique male participation",
            "sample_values": "101, 102, 103"
          },
          "Female_ID": {
            "data_type": "INTEGER",
            "business_meaning": "Identifier for the female participant",
            "optimization_purpose": "Used to ensure unique female participation",
            "sample_values": "201, 202, 203"
          },
          "total_weddings": {
            "data_type": "INTEGER",
            "business_meaning": "Total number of weddings held",
            "optimization_purpose": "Objective coefficient for maximizing weddings",
            "sample_values": "10, 15, 20"
          }
        }
      },
      "ChurchCapacity": {
        "business_purpose": "Stores capacity limits for each church",
        "optimization_role": "constraint_bounds",
        "columns": {
          "church_id": {
            "data_type": "INTEGER",
            "business_meaning": "Identifier for the church",
            "optimization_purpose": "Links to church capacity constraints",
            "sample_values": "1, 2, 3"
          },
          "capacity": {
            "data_type": "INTEGER",
            "business_meaning": "Maximum number of weddings a church can host",
            "optimization_purpose": "Constraint bound for church capacity",
            "sample_values": "100, 150, 200"
          }
        }
      },
      "ParticipantConstraints": {
        "business_purpose": "Ensures unique participation of individuals in weddings",
        "optimization_role": "constraint_bounds",
        "columns": {
          "participant_id": {
            "data_type": "INTEGER",
            "business_meaning": "Identifier for the participant",
            "optimization_purpose": "Ensures unique participation",
            "sample_values": "101, 102, 201"
          },
          "constraint_type": {
            "data_type": "STRING",
            "business_meaning": "Type of participation constraint",
            "optimization_purpose": "Defines the constraint logic",
            "sample_values": "male, female"
          }
        }
      }
    }
  },
  "data_mapping": {
    "objective_sources": [
      "wedding.total_weddings"
    ],
    "constraint_sources": [
      "ChurchCapacity.capacity",
      "ParticipantConstraints.participant_id"
    ],
    "sample_data_rows": {
      "wedding": 5,
      "ChurchCapacity": 3,
      "ParticipantConstraints": 6
    }
  },
  "validation": {
    "schema_complete": true,
    "data_consistent": true,
    "math_traceable": true,
    "business_aligned": true,
    "follows_db_standards": true
  }
}
