Iteration 1 - DATA_ENGINEER
Sequence: 2
Timestamp: 2025-07-27 22:04:33

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": "entertainment_awards",
  "iteration": 0,
  "business_context": "An entertainment festival wants to maximize the number of successful nominations (wins) for artworks across different festivals while considering the audience size as a factor for prioritization.",
  "optimization_problem_description": "The goal is to maximize the number of successful nominations for artworks at various festivals, taking into account the audience size as a weight for each festival. The decision variables will determine whether an artwork is nominated at a particular festival, subject to constraints on the number of nominations per artwork and the capacity of each festival.",
  "optimization_formulation": {
    "objective": "maximize sum(audience_size[festival_id] * nomination_success[artwork_id, festival_id])",
    "decision_variables": "nomination_success[artwork_id, festival_id] (binary: 1 if artwork wins at festival, 0 otherwise)",
    "constraints": [
      "sum(nomination_success[artwork_id, festival_id] for all festival_id) <= max_nominations_per_artwork for each artwork_id",
      "sum(nomination_success[artwork_id, festival_id] for all artwork_id) <= max_capacity_per_festival for each festival_id"
    ]
  },
  "current_optimization_to_schema_mapping": {
    "objective_coefficients": {
      "audience_size[festival_id]": {
        "currently_mapped_to": "festival_detail.Num_of_Audience",
        "mapping_adequacy": "good",
        "description": "Represents the audience size at each festival, used as a weight in the objective function"
      }
    },
    "constraint_bounds": {
      "max_nominations_per_artwork": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "Maximum number of nominations allowed per artwork"
      },
      "max_capacity_per_festival": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "Maximum number of artworks that can be nominated at each festival"
      }
    },
    "decision_variables": {
      "nomination_success[artwork_id, festival_id]": {
        "currently_mapped_to": "nomination.Result",
        "mapping_adequacy": "partial",
        "description": "Binary variable indicating if an artwork wins at a festival",
        "variable_type": "binary"
      }
    }
  },
  "missing_optimization_requirements": [
    "Data on max_nominations_per_artwork",
    "Data on max_capacity_per_festival"
  ],
  "iteration_status": {
    "complete": false,
    "confidence": "medium",
    "next_focus": "Refine constraints and ensure all necessary data is available for optimization"
  }
}





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

JSON STRUCTURE REQUIRED:

{
  "database_id": "entertainment_awards",
  "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": "entertainment_awards",
  "iteration": 1,
  "implementation_summary": "Schema changes include creating new tables for missing constraint bounds, modifying existing tables to improve mapping adequacy, and updating business configuration logic for scalar parameters and formulas.",
  "or_requirements_analysis": {
    "mapping_gaps_identified": [
      "max_nominations_per_artwork not mapped",
      "max_capacity_per_festival not mapped",
      "Partial mapping for nomination_success"
    ],
    "missing_data_requirements": [
      "Data on max_nominations_per_artwork",
      "Data on max_capacity_per_festival"
    ],
    "business_configuration_logic_needs": [
      "max_nominations_per_artwork",
      "max_capacity_per_festival"
    ]
  },
  "schema_adjustment_decisions": {
    "tables_to_delete": [],
    "tables_to_create": [
      {
        "table_name": "artwork_constraints",
        "purpose": "constraint_bounds",
        "business_meaning": "Stores maximum nominations allowed per artwork"
      },
      {
        "table_name": "festival_constraints",
        "purpose": "constraint_bounds",
        "business_meaning": "Stores maximum capacity of nominations per festival"
      }
    ],
    "tables_to_modify": [
      {
        "table_name": "nomination",
        "changes": "Add binary constraint to Result column",
        "reason": "Ensure Result column accurately represents binary decision variable"
      }
    ]
  },
  "business_configuration_logic_updates": {
    "configuration_parameters": {
      "max_nominations_per_artwork": {
        "sample_value": "5",
        "data_type": "INTEGER",
        "business_meaning": "Maximum number of nominations allowed per artwork",
        "optimization_role": "Used as a constraint bound in optimization model",
        "configuration_type": "scalar_parameter"
      },
      "max_capacity_per_festival": {
        "sample_value": "100",
        "data_type": "INTEGER",
        "business_meaning": "Maximum number of artworks that can be nominated at each festival",
        "optimization_role": "Used as a constraint bound in optimization model",
        "configuration_type": "scalar_parameter"
      }
    },
    "updates_rationale": "These parameters are better suited for configuration logic due to their scalar nature and lack of need for tabular representation."
  },
  "implemented_optimization_mapping": {
    "objective_coefficients_mapping": {
      "audience_size[festival_id]": "festival_detail.Num_of_Audience"
    },
    "constraint_bounds_mapping": {
      "max_nominations_per_artwork": "business_configuration_logic.max_nominations_per_artwork",
      "max_capacity_per_festival": "business_configuration_logic.max_capacity_per_festival"
    },
    "decision_variables_mapping": {
      "nomination_success[artwork_id, festival_id]": "nomination.Result"
    }
  },
  "data_dictionary": {
    "tables": {
      "festival_detail": {
        "business_purpose": "Stores details about each festival",
        "optimization_role": "objective_coefficients",
        "columns": {
          "Num_of_Audience": {
            "data_type": "INTEGER",
            "business_meaning": "Number of audience members at the festival",
            "optimization_purpose": "Used as a weight in the objective function",
            "sample_values": "1000, 5000, 10000"
          }
        }
      },
      "nomination": {
        "business_purpose": "Stores nomination results for artworks at festivals",
        "optimization_role": "decision_variables",
        "columns": {
          "Result": {
            "data_type": "BOOLEAN",
            "business_meaning": "Indicates if an artwork wins at a festival",
            "optimization_purpose": "Represents binary decision variable",
            "sample_values": "0, 1"
          }
        }
      },
      "artwork_constraints": {
        "business_purpose": "Stores constraints related to artworks",
        "optimization_role": "constraint_bounds",
        "columns": {
          "max_nominations": {
            "data_type": "INTEGER",
            "business_meaning": "Maximum nominations allowed per artwork",
            "optimization_purpose": "Constraint bound for nominations",
            "sample_values": "3, 5, 7"
          }
        }
      },
      "festival_constraints": {
        "business_purpose": "Stores constraints related to festivals",
        "optimization_role": "constraint_bounds",
        "columns": {
          "max_capacity": {
            "data_type": "INTEGER",
            "business_meaning": "Maximum capacity of nominations per festival",
            "optimization_purpose": "Constraint bound for festival capacity",
            "sample_values": "50, 100, 150"
          }
        }
      }
    }
  },
  "data_mapping": {
    "objective_sources": [
      "festival_detail.Num_of_Audience"
    ],
    "constraint_sources": [
      "business_configuration_logic.max_nominations_per_artwork",
      "business_configuration_logic.max_capacity_per_festival"
    ],
    "sample_data_rows": {
      "festival_detail": 3,
      "nomination": 5,
      "artwork_constraints": 3,
      "festival_constraints": 3
    }
  },
  "validation": {
    "schema_complete": true,
    "data_consistent": true,
    "math_traceable": true,
    "business_aligned": true,
    "follows_db_standards": true
  }
}
