Iteration 1 - DATA_ENGINEER
Sequence: 2
Timestamp: 2025-07-25 22:30:24

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 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": "roller_coaster",
  "iteration": 0,
  "business_context": "A theme park chain wants to optimize the distribution of roller coasters across its parks in different countries to maximize visitor satisfaction while respecting budget and space constraints.",
  "optimization_problem_description": "The objective is to maximize the total visitor satisfaction score across all parks by deciding how many roller coasters of each type to install in each park, considering constraints on budget, space, and the number of roller coasters per park.",
  "optimization_formulation": {
    "objective": "maximize \u2211(satisfaction_score[park, coaster_type] \u00d7 num_coasters[park, coaster_type])",
    "decision_variables": "num_coasters[park, coaster_type] (integer)",
    "constraints": [
      "\u2211(cost[coaster_type] \u00d7 num_coasters[park, coaster_type]) \u2264 budget[park] for each park",
      "\u2211(space[coaster_type] \u00d7 num_coasters[park, coaster_type]) \u2264 available_space[park] for each park",
      "num_coasters[park, coaster_type] \u2265 0 for each park and coaster_type",
      "\u2211(num_coasters[park, coaster_type]) \u2264 max_coasters[park] for each park"
    ]
  },
  "current_optimization_to_schema_mapping": {
    "objective_coefficients": {
      "satisfaction_score[park, coaster_type]": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "Visitor satisfaction score for each roller coaster type in each park"
      }
    },
    "constraint_bounds": {
      "budget[park]": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "Budget limit for each park"
      },
      "available_space[park]": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "Available space for roller coasters in each park"
      },
      "max_coasters[park]": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "Maximum number of roller coasters allowed in each park"
      }
    },
    "decision_variables": {
      "num_coasters[park, coaster_type]": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "Number of roller coasters of each type to install in each park",
        "variable_type": "integer"
      }
    }
  },
  "missing_optimization_requirements": [
    "Visitor satisfaction scores for each roller coaster type in each park",
    "Budget limits for each park",
    "Available space for roller coasters in each park",
    "Maximum number of roller coasters allowed in each park",
    "Cost of each roller coaster type",
    "Space required for each roller coaster type"
  ],
  "iteration_status": {
    "complete": false,
    "confidence": "low",
    "next_focus": "Gather missing data on visitor satisfaction scores, budget limits, available space, and maximum number of roller coasters per park."
  }
}





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

JSON STRUCTURE REQUIRED:

{
  "database_id": "roller_coaster",
  "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": "roller_coaster",
  "iteration": 1,
  "implementation_summary": "Schema changes include creating new tables for visitor satisfaction scores, budget limits, available space, and maximum roller coasters per park. Configuration logic updates include scalar parameters for cost and space requirements of roller coaster types.",
  "or_requirements_analysis": {
    "mapping_gaps_identified": [
      "satisfaction_score[park, coaster_type]",
      "budget[park]",
      "available_space[park]",
      "max_coasters[park]"
    ],
    "missing_data_requirements": [
      "Visitor satisfaction scores for each roller coaster type in each park",
      "Budget limits for each park",
      "Available space for roller coasters in each park",
      "Maximum number of roller coasters allowed in each park",
      "Cost of each roller coaster type",
      "Space required for each roller coaster type"
    ],
    "business_configuration_logic_needs": [
      "Cost of each roller coaster type",
      "Space required for each roller coaster type"
    ]
  },
  "schema_adjustment_decisions": {
    "tables_to_delete": [],
    "tables_to_create": [
      {
        "table_name": "visitor_satisfaction_scores",
        "purpose": "objective_coefficients",
        "business_meaning": "Visitor satisfaction score for each roller coaster type in each park"
      },
      {
        "table_name": "park_budgets",
        "purpose": "constraint_bounds",
        "business_meaning": "Budget limit for each park"
      },
      {
        "table_name": "park_available_space",
        "purpose": "constraint_bounds",
        "business_meaning": "Available space for roller coasters in each park"
      },
      {
        "table_name": "park_max_coasters",
        "purpose": "constraint_bounds",
        "business_meaning": "Maximum number of roller coasters allowed in each park"
      }
    ],
    "tables_to_modify": []
  },
  "business_configuration_logic_updates": {
    "configuration_parameters": {
      "coaster_cost": {
        "sample_value": 500000,
        "data_type": "INTEGER",
        "business_meaning": "Cost of each roller coaster type",
        "optimization_role": "Used in budget constraint calculation",
        "configuration_type": "scalar_parameter"
      },
      "coaster_space": {
        "sample_value": 2000,
        "data_type": "INTEGER",
        "business_meaning": "Space required for each roller coaster type",
        "optimization_role": "Used in space constraint calculation",
        "configuration_type": "scalar_parameter"
      }
    },
    "updates_rationale": "Cost and space requirements are scalar values better suited for configuration logic than tables."
  },
  "implemented_optimization_mapping": {
    "objective_coefficients_mapping": {
      "satisfaction_score[park, coaster_type]": "visitor_satisfaction_scores.score"
    },
    "constraint_bounds_mapping": {
      "budget[park]": "park_budgets.budget",
      "available_space[park]": "park_available_space.space",
      "max_coasters[park]": "park_max_coasters.max_coasters"
    },
    "decision_variables_mapping": {
      "num_coasters[park, coaster_type]": "visitor_satisfaction_scores.num_coasters"
    }
  },
  "data_dictionary": {
    "tables": {
      "visitor_satisfaction_scores": {
        "business_purpose": "Visitor satisfaction score for each roller coaster type in each park",
        "optimization_role": "objective_coefficients",
        "columns": {
          "park_id": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for each park",
            "optimization_purpose": "Index for park in optimization model",
            "sample_values": "1, 2, 3"
          },
          "coaster_type": {
            "data_type": "STRING",
            "business_meaning": "Type of roller coaster",
            "optimization_purpose": "Index for coaster type in optimization model",
            "sample_values": "Wooden, Steel, Inverted"
          },
          "score": {
            "data_type": "FLOAT",
            "business_meaning": "Visitor satisfaction score",
            "optimization_purpose": "Coefficient in objective function",
            "sample_values": "8.5, 9.0, 7.5"
          },
          "num_coasters": {
            "data_type": "INTEGER",
            "business_meaning": "Number of roller coasters of this type in the park",
            "optimization_purpose": "Decision variable in optimization model",
            "sample_values": "2, 3, 1"
          }
        }
      },
      "park_budgets": {
        "business_purpose": "Budget limit for each park",
        "optimization_role": "constraint_bounds",
        "columns": {
          "park_id": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for each park",
            "optimization_purpose": "Index for park in optimization model",
            "sample_values": "1, 2, 3"
          },
          "budget": {
            "data_type": "INTEGER",
            "business_meaning": "Budget limit for the park",
            "optimization_purpose": "Bound in budget constraint",
            "sample_values": "1000000, 1500000, 2000000"
          }
        }
      },
      "park_available_space": {
        "business_purpose": "Available space for roller coasters in each park",
        "optimization_role": "constraint_bounds",
        "columns": {
          "park_id": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for each park",
            "optimization_purpose": "Index for park in optimization model",
            "sample_values": "1, 2, 3"
          },
          "space": {
            "data_type": "INTEGER",
            "business_meaning": "Available space for roller coasters",
            "optimization_purpose": "Bound in space constraint",
            "sample_values": "10000, 15000, 20000"
          }
        }
      },
      "park_max_coasters": {
        "business_purpose": "Maximum number of roller coasters allowed in each park",
        "optimization_role": "constraint_bounds",
        "columns": {
          "park_id": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for each park",
            "optimization_purpose": "Index for park in optimization model",
            "sample_values": "1, 2, 3"
          },
          "max_coasters": {
            "data_type": "INTEGER",
            "business_meaning": "Maximum number of roller coasters allowed",
            "optimization_purpose": "Bound in maximum roller coasters constraint",
            "sample_values": "5, 7, 10"
          }
        }
      }
    }
  },
  "data_mapping": {
    "objective_sources": [
      "visitor_satisfaction_scores.score"
    ],
    "constraint_sources": [
      "park_budgets.budget",
      "park_available_space.space",
      "park_max_coasters.max_coasters"
    ],
    "sample_data_rows": {
      "visitor_satisfaction_scores": 3,
      "park_budgets": 3,
      "park_available_space": 3,
      "park_max_coasters": 3
    }
  },
  "validation": {
    "schema_complete": true,
    "data_consistent": true,
    "math_traceable": true,
    "business_aligned": true,
    "follows_db_standards": true
  }
}
