Iteration 1 - DATA_ENGINEER
Sequence: 2
Timestamp: 2025-07-27 22:43:48

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": "gymnast",
  "iteration": 0,
  "business_context": "A gymnastics competition organizer wants to maximize the total points scored by gymnasts across different events while ensuring that each gymnast participates in a limited number of events due to time constraints.",
  "optimization_problem_description": "The goal is to allocate gymnasts to events in such a way that the total points scored across all events is maximized, subject to constraints on the number of events each gymnast can participate in and the total number of gymnasts allowed per event.",
  "optimization_formulation": {
    "objective": "maximize total_points = \u2211(Floor_Exercise_Points[i] + Pommel_Horse_Points[i] + Rings_Points[i] + Vault_Points[i] + Parallel_Bars_Points[i] + Horizontal_Bar_Points[i]) * x[i]",
    "decision_variables": "x[i] is a binary variable indicating whether gymnast i participates in the event (1 if yes, 0 if no)",
    "constraints": [
      "\u2211 x[i] <= max_events_per_gymnast for each gymnast i",
      "\u2211 x[i] <= max_gymnasts_per_event for each event",
      "x[i] \u2208 {0, 1} for all i"
    ]
  },
  "current_optimization_to_schema_mapping": {
    "objective_coefficients": {
      "Floor_Exercise_Points[i]": {
        "currently_mapped_to": "gymnast.Floor_Exercise_Points",
        "mapping_adequacy": "good",
        "description": "Points scored by gymnast i in the Floor Exercise event"
      },
      "Pommel_Horse_Points[i]": {
        "currently_mapped_to": "gymnast.Pommel_Horse_Points",
        "mapping_adequacy": "good",
        "description": "Points scored by gymnast i in the Pommel Horse event"
      },
      "Rings_Points[i]": {
        "currently_mapped_to": "gymnast.Rings_Points",
        "mapping_adequacy": "good",
        "description": "Points scored by gymnast i in the Rings event"
      },
      "Vault_Points[i]": {
        "currently_mapped_to": "gymnast.Vault_Points",
        "mapping_adequacy": "good",
        "description": "Points scored by gymnast i in the Vault event"
      },
      "Parallel_Bars_Points[i]": {
        "currently_mapped_to": "gymnast.Parallel_Bars_Points",
        "mapping_adequacy": "good",
        "description": "Points scored by gymnast i in the Parallel Bars event"
      },
      "Horizontal_Bar_Points[i]": {
        "currently_mapped_to": "gymnast.Horizontal_Bar_Points",
        "mapping_adequacy": "good",
        "description": "Points scored by gymnast i in the Horizontal Bar event"
      }
    },
    "constraint_bounds": {
      "max_events_per_gymnast": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "Maximum number of events a single gymnast can participate in"
      },
      "max_gymnasts_per_event": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "Maximum number of gymnasts allowed to participate in a single event"
      }
    },
    "decision_variables": {
      "x[i]": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "Binary variable indicating participation of gymnast i in an event",
        "variable_type": "binary"
      }
    }
  },
  "missing_optimization_requirements": [
    "Data on max_events_per_gymnast",
    "Data on max_gymnasts_per_event"
  ],
  "iteration_status": {
    "complete": false,
    "confidence": "medium",
    "next_focus": "Gather missing data on constraints and refine decision variable mapping"
  }
}





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

JSON STRUCTURE REQUIRED:

{
  "database_id": "gymnast",
  "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": "gymnast",
  "iteration": 1,
  "implementation_summary": "Schema changes include creating tables for decision variables and constraint bounds, and moving scalar parameters to configuration logic. Adjustments ensure alignment with OR expert's requirements and optimization model.",
  "or_requirements_analysis": {
    "mapping_gaps_identified": [
      "Missing mapping for max_events_per_gymnast",
      "Missing mapping for max_gymnasts_per_event",
      "Missing mapping for decision variable x[i]"
    ],
    "missing_data_requirements": [
      "Data on max_events_per_gymnast",
      "Data on max_gymnasts_per_event"
    ],
    "business_configuration_logic_needs": [
      "max_events_per_gymnast",
      "max_gymnasts_per_event"
    ]
  },
  "schema_adjustment_decisions": {
    "tables_to_delete": [],
    "tables_to_create": [
      {
        "table_name": "decision_variables",
        "purpose": "decision_variables",
        "business_meaning": "Stores binary decision variables indicating gymnast participation in events"
      },
      {
        "table_name": "constraint_bounds",
        "purpose": "constraint_bounds",
        "business_meaning": "Stores constraint bounds for maximum events per gymnast and gymnasts per event"
      }
    ],
    "tables_to_modify": [
      {
        "table_name": "gymnast",
        "changes": "Add columns for decision variables and constraint bounds",
        "reason": "To address missing mappings for decision variables and constraint bounds"
      }
    ]
  },
  "business_configuration_logic_updates": {
    "configuration_parameters": {
      "max_events_per_gymnast": {
        "sample_value": "3",
        "data_type": "INTEGER",
        "business_meaning": "Maximum number of events a single gymnast can participate in",
        "optimization_role": "Constraint bound in optimization model",
        "configuration_type": "scalar_parameter"
      },
      "max_gymnasts_per_event": {
        "sample_value": "5",
        "data_type": "INTEGER",
        "business_meaning": "Maximum number of gymnasts allowed to participate in a single event",
        "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 due to their singular nature."
  },
  "implemented_optimization_mapping": {
    "objective_coefficients_mapping": {
      "Floor_Exercise_Points[i]": "gymnast.Floor_Exercise_Points",
      "Pommel_Horse_Points[i]": "gymnast.Pommel_Horse_Points",
      "Rings_Points[i]": "gymnast.Rings_Points",
      "Vault_Points[i]": "gymnast.Vault_Points",
      "Parallel_Bars_Points[i]": "gymnast.Parallel_Bars_Points",
      "Horizontal_Bar_Points[i]": "gymnast.Horizontal_Bar_Points"
    },
    "constraint_bounds_mapping": {
      "max_events_per_gymnast": "business_configuration_logic.max_events_per_gymnast",
      "max_gymnasts_per_event": "business_configuration_logic.max_gymnasts_per_event"
    },
    "decision_variables_mapping": {
      "x[i]": "decision_variables.participation"
    }
  },
  "data_dictionary": {
    "tables": {
      "gymnast": {
        "business_purpose": "Stores gymnast performance data across events",
        "optimization_role": "objective_coefficients",
        "columns": {
          "Floor_Exercise_Points": {
            "data_type": "INTEGER",
            "business_meaning": "Points scored by gymnast in Floor Exercise",
            "optimization_purpose": "Objective coefficient",
            "sample_values": "10, 15, 20"
          },
          "Pommel_Horse_Points": {
            "data_type": "INTEGER",
            "business_meaning": "Points scored by gymnast in Pommel Horse",
            "optimization_purpose": "Objective coefficient",
            "sample_values": "8, 12, 18"
          },
          "Rings_Points": {
            "data_type": "INTEGER",
            "business_meaning": "Points scored by gymnast in Rings",
            "optimization_purpose": "Objective coefficient",
            "sample_values": "9, 14, 19"
          },
          "Vault_Points": {
            "data_type": "INTEGER",
            "business_meaning": "Points scored by gymnast in Vault",
            "optimization_purpose": "Objective coefficient",
            "sample_values": "11, 16, 21"
          },
          "Parallel_Bars_Points": {
            "data_type": "INTEGER",
            "business_meaning": "Points scored by gymnast in Parallel Bars",
            "optimization_purpose": "Objective coefficient",
            "sample_values": "7, 13, 17"
          },
          "Horizontal_Bar_Points": {
            "data_type": "INTEGER",
            "business_meaning": "Points scored by gymnast in Horizontal Bar",
            "optimization_purpose": "Objective coefficient",
            "sample_values": "6, 10, 15"
          }
        }
      },
      "decision_variables": {
        "business_purpose": "Stores decision variables for gymnast participation",
        "optimization_role": "decision_variables",
        "columns": {
          "participation": {
            "data_type": "BOOLEAN",
            "business_meaning": "Indicates if gymnast participates in an event",
            "optimization_purpose": "Decision variable",
            "sample_values": "true, false"
          }
        }
      },
      "constraint_bounds": {
        "business_purpose": "Stores constraint bounds for optimization",
        "optimization_role": "constraint_bounds",
        "columns": {
          "max_events_per_gymnast": {
            "data_type": "INTEGER",
            "business_meaning": "Max events a gymnast can participate in",
            "optimization_purpose": "Constraint bound",
            "sample_values": "3"
          },
          "max_gymnasts_per_event": {
            "data_type": "INTEGER",
            "business_meaning": "Max gymnasts per event",
            "optimization_purpose": "Constraint bound",
            "sample_values": "5"
          }
        }
      }
    }
  },
  "data_mapping": {
    "objective_sources": [
      "gymnast.Floor_Exercise_Points",
      "gymnast.Pommel_Horse_Points",
      "gymnast.Rings_Points",
      "gymnast.Vault_Points",
      "gymnast.Parallel_Bars_Points",
      "gymnast.Horizontal_Bar_Points"
    ],
    "constraint_sources": [
      "business_configuration_logic.max_events_per_gymnast",
      "business_configuration_logic.max_gymnasts_per_event"
    ],
    "sample_data_rows": {
      "gymnast": 6,
      "decision_variables": 6,
      "constraint_bounds": 2
    }
  },
  "validation": {
    "schema_complete": true,
    "data_consistent": true,
    "math_traceable": true,
    "business_aligned": true,
    "follows_db_standards": true
  }
}
