Iteration 3 - DATA_ENGINEER
Sequence: 6
Timestamp: 2025-07-25 22:46:46

Prompt:
You are a senior database architect implementing schema modifications for iteration 3. 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 3):
{
  "database_id": "entertainment_awards",
  "iteration": 2,
  "business_context": "A film festival aims to maximize the total audience engagement by selecting a subset of artworks to nominate, considering constraints such as the number of nominations per festival and the diversity of artwork types.",
  "optimization_problem_description": "Maximize the total audience engagement by selecting a subset of artworks to nominate, subject to constraints on the maximum number of nominations per festival and ensuring a minimum diversity of artwork types.",
  "optimization_formulation": {
    "objective": "maximize \u2211(Engagement_Score[Artwork_ID, Festival_ID] \u00d7 Nomination_Decision[Artwork_ID, Festival_ID])",
    "decision_variables": "Nomination_Decision[Artwork_ID, Festival_ID] \u2208 {0, 1}",
    "constraints": [
      "\u2211(Nomination_Decision[Artwork_ID, Festival_ID]) \u2264 max_nominations[Festival_ID] for each Festival_ID",
      "\u2211(diversity_score[Type] \u00d7 Nomination_Decision[Artwork_ID, Festival_ID]) \u2265 minimum_diversity_score"
    ]
  },
  "current_optimization_to_schema_mapping": {
    "objective_coefficients": {
      "Engagement_Score[Artwork_ID, Festival_ID]": {
        "currently_mapped_to": "engagement_scores.score",
        "mapping_adequacy": "good",
        "description": "Engagement score for the artwork at the festival"
      }
    },
    "constraint_bounds": {
      "max_nominations[Festival_ID]": {
        "currently_mapped_to": "festival_nominations.max_nominations",
        "mapping_adequacy": "good",
        "description": "Maximum number of nominations allowed per festival"
      },
      "minimum_diversity_score": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "Minimum required diversity score for nominated artworks"
      }
    },
    "decision_variables": {
      "Nomination_Decision[Artwork_ID, Festival_ID]": {
        "currently_mapped_to": "nomination_decisions.decision",
        "mapping_adequacy": "good",
        "description": "Binary decision indicating whether an artwork is nominated at a festival",
        "variable_type": "binary"
      }
    }
  },
  "missing_optimization_requirements": [
    "minimum_diversity_score"
  ],
  "iteration_status": {
    "complete": false,
    "confidence": "medium",
    "next_focus": "Define and map the missing constraint bound for minimum diversity score."
  }
}


CURRENT DATABASE SCHEMA:
```sql
-- Iteration 2 Database Schema
-- Objective: Schema changes include creating a table for nomination decisions and updating the data dictionary. Configuration logic remains unchanged as it already meets the requirements.

CREATE TABLE engagement_scores (
  Artwork_ID INTEGER,
  Festival_ID INTEGER,
  score FLOAT
);

CREATE TABLE festival_nominations (
  Festival_ID INTEGER,
  max_nominations INTEGER
);

CREATE TABLE artwork_types (
  Type STRING,
  diversity_score FLOAT
);

CREATE TABLE nomination_decisions (
  Artwork_ID INTEGER,
  Festival_ID INTEGER,
  decision BOOLEAN
);


```


CURRENT DATA DICTIONARY:
{
  "tables": {
    "engagement_scores": {
      "business_purpose": "Engagement scores for artworks at festivals",
      "optimization_role": "objective_coefficients",
      "columns": {
        "Artwork_ID": {
          "data_type": "INTEGER",
          "business_meaning": "Unique identifier for the artwork",
          "optimization_purpose": "Index for engagement score",
          "sample_values": "1, 2, 3"
        },
        "Festival_ID": {
          "data_type": "INTEGER",
          "business_meaning": "Unique identifier for the festival",
          "optimization_purpose": "Index for engagement score",
          "sample_values": "1, 2, 3"
        },
        "score": {
          "data_type": "FLOAT",
          "business_meaning": "Engagement score for the artwork at the festival",
          "optimization_purpose": "Objective coefficient in optimization model",
          "sample_values": "0.5, 0.7, 0.9"
        }
      }
    },
    "festival_nominations": {
      "business_purpose": "Maximum number of nominations allowed per festival",
      "optimization_role": "constraint_bounds",
      "columns": {
        "Festival_ID": {
          "data_type": "INTEGER",
          "business_meaning": "Unique identifier for the festival",
          "optimization_purpose": "Index for max nominations",
          "sample_values": "1, 2, 3"
        },
        "max_nominations": {
          "data_type": "INTEGER",
          "business_meaning": "Maximum number of nominations allowed per festival",
          "optimization_purpose": "Constraint bound in optimization model",
          "sample_values": "5, 6, 7"
        }
      }
    },
    "artwork_types": {
      "business_purpose": "Types of artworks and their diversity scores",
      "optimization_role": "business_data",
      "columns": {
        "Type": {
          "data_type": "STRING",
          "business_meaning": "Type of artwork",
          "optimization_purpose": "Index for type diversity",
          "sample_values": "Film, Short, Documentary"
        },
        "diversity_score": {
          "data_type": "FLOAT",
          "business_meaning": "Diversity score for the artwork type",
          "optimization_purpose": "Used in diversity constraint",
          "sample_values": "0.8, 0.9, 1.0"
        }
      }
    },
    "nomination_decisions": {
      "business_purpose": "Binary decision variable indicating whether an artwork is nominated at a festival",
      "optimization_role": "decision_variables",
      "columns": {
        "Artwork_ID": {
          "data_type": "INTEGER",
          "business_meaning": "Unique identifier for the artwork",
          "optimization_purpose": "Index for nomination decision",
          "sample_values": "1, 2, 3"
        },
        "Festival_ID": {
          "data_type": "INTEGER",
          "business_meaning": "Unique identifier for the festival",
          "optimization_purpose": "Index for nomination decision",
          "sample_values": "1, 2, 3"
        },
        "decision": {
          "data_type": "BOOLEAN",
          "business_meaning": "Binary decision indicating nomination",
          "optimization_purpose": "Decision variable in optimization model",
          "sample_values": "true, false"
        }
      }
    }
  }
}



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

JSON STRUCTURE REQUIRED:

{
  "database_id": "entertainment_awards",
  "iteration": 3,
  "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": 3,
  "implementation_summary": "Schema changes include adding a minimum diversity score constraint to the business configuration logic and updating the data dictionary to reflect this addition. No new tables were created or deleted as the existing schema adequately supports the optimization requirements.",
  "or_requirements_analysis": {
    "mapping_gaps_identified": [
      "minimum_diversity_score constraint is missing"
    ],
    "missing_data_requirements": [
      "minimum_diversity_score"
    ],
    "business_configuration_logic_needs": [
      "minimum_diversity_score is better suited as a scalar parameter in configuration logic"
    ]
  },
  "schema_adjustment_decisions": {
    "tables_to_delete": [],
    "tables_to_create": [],
    "tables_to_modify": []
  },
  "business_configuration_logic_updates": {
    "configuration_parameters": {
      "minimum_diversity_score": {
        "sample_value": 0.7,
        "data_type": "FLOAT",
        "business_meaning": "Minimum required diversity score for nominated artworks",
        "optimization_role": "Constraint bound in optimization model",
        "configuration_type": "scalar_parameter"
      }
    },
    "updates_rationale": "The minimum_diversity_score is a single scalar value that does not require a table and is better managed in the business configuration logic."
  },
  "implemented_optimization_mapping": {
    "objective_coefficients_mapping": {
      "Engagement_Score[Artwork_ID, Festival_ID]": "engagement_scores.score"
    },
    "constraint_bounds_mapping": {
      "max_nominations[Festival_ID]": "festival_nominations.max_nominations",
      "minimum_diversity_score": "business_configuration_logic.minimum_diversity_score"
    },
    "decision_variables_mapping": {
      "Nomination_Decision[Artwork_ID, Festival_ID]": "nomination_decisions.decision"
    }
  },
  "data_dictionary": {
    "tables": {
      "engagement_scores": {
        "business_purpose": "Engagement scores for artworks at festivals",
        "optimization_role": "objective_coefficients",
        "columns": {
          "Artwork_ID": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for the artwork",
            "optimization_purpose": "Index for engagement score",
            "sample_values": "1, 2, 3"
          },
          "Festival_ID": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for the festival",
            "optimization_purpose": "Index for engagement score",
            "sample_values": "1, 2, 3"
          },
          "score": {
            "data_type": "FLOAT",
            "business_meaning": "Engagement score for the artwork at the festival",
            "optimization_purpose": "Objective coefficient in optimization model",
            "sample_values": "0.5, 0.7, 0.9"
          }
        }
      },
      "festival_nominations": {
        "business_purpose": "Maximum number of nominations allowed per festival",
        "optimization_role": "constraint_bounds",
        "columns": {
          "Festival_ID": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for the festival",
            "optimization_purpose": "Index for max nominations",
            "sample_values": "1, 2, 3"
          },
          "max_nominations": {
            "data_type": "INTEGER",
            "business_meaning": "Maximum number of nominations allowed per festival",
            "optimization_purpose": "Constraint bound in optimization model",
            "sample_values": "5, 6, 7"
          }
        }
      },
      "artwork_types": {
        "business_purpose": "Types of artworks and their diversity scores",
        "optimization_role": "business_data",
        "columns": {
          "Type": {
            "data_type": "STRING",
            "business_meaning": "Type of artwork",
            "optimization_purpose": "Index for type diversity",
            "sample_values": "Film, Short, Documentary"
          },
          "diversity_score": {
            "data_type": "FLOAT",
            "business_meaning": "Diversity score for the artwork type",
            "optimization_purpose": "Used in diversity constraint",
            "sample_values": "0.8, 0.9, 1.0"
          }
        }
      },
      "nomination_decisions": {
        "business_purpose": "Binary decision variable indicating whether an artwork is nominated at a festival",
        "optimization_role": "decision_variables",
        "columns": {
          "Artwork_ID": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for the artwork",
            "optimization_purpose": "Index for nomination decision",
            "sample_values": "1, 2, 3"
          },
          "Festival_ID": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for the festival",
            "optimization_purpose": "Index for nomination decision",
            "sample_values": "1, 2, 3"
          },
          "decision": {
            "data_type": "BOOLEAN",
            "business_meaning": "Binary decision indicating nomination",
            "optimization_purpose": "Decision variable in optimization model",
            "sample_values": "true, false"
          }
        }
      }
    }
  },
  "data_mapping": {
    "objective_sources": [
      "engagement_scores.score"
    ],
    "constraint_sources": [
      "festival_nominations.max_nominations",
      "business_configuration_logic.minimum_diversity_score"
    ],
    "sample_data_rows": {
      "engagement_scores": 3,
      "festival_nominations": 3,
      "artwork_types": 3,
      "nomination_decisions": 3
    }
  },
  "validation": {
    "schema_complete": true,
    "data_consistent": true,
    "math_traceable": true,
    "business_aligned": true,
    "follows_db_standards": true
  }
}
