Iteration 1 - DATA_ENGINEER
Sequence: 2
Timestamp: 2025-07-27 21:59:36

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": "music_4",
  "iteration": 0,
  "business_context": "A music festival organizer wants to maximize the exposure of artists by selecting the optimal set of artists to perform at a festival, considering their popularity and availability.",
  "optimization_problem_description": "The goal is to maximize the total weeks on top of the charts for the selected artists while ensuring that the total number of artists selected does not exceed the festival's capacity and that each artist is available on the festival date.",
  "optimization_formulation": {
    "objective": "maximize total_weeks_on_top = \u2211(Weeks_on_Top[i] * x[i])",
    "decision_variables": "x[i] = 1 if artist i is selected to perform, 0 otherwise (binary)",
    "constraints": [
      "\u2211(x[i]) \u2264 festival_capacity",
      "x[i] = 0 if artist i is not available on the festival date"
    ]
  },
  "current_optimization_to_schema_mapping": {
    "objective_coefficients": {
      "Weeks_on_Top[i]": {
        "currently_mapped_to": "volume.Weeks_on_Top",
        "mapping_adequacy": "good",
        "description": "Represents the popularity of artist i based on weeks on top of the charts"
      }
    },
    "constraint_bounds": {
      "festival_capacity": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "Maximum number of artists that can be selected for the festival"
      }
    },
    "decision_variables": {
      "x[i]": {
        "currently_mapped_to": "artist.Artist_ID",
        "mapping_adequacy": "partial",
        "description": "Binary variable indicating if artist i is selected",
        "variable_type": "binary"
      }
    }
  },
  "missing_optimization_requirements": [
    "Festival capacity data",
    "Artist availability on the festival date"
  ],
  "iteration_status": {
    "complete": false,
    "confidence": "medium",
    "next_focus": "Incorporate artist availability and festival capacity into the model"
  }
}





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

JSON STRUCTURE REQUIRED:

{
  "database_id": "music_4",
  "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": "music_4",
  "iteration": 1,
  "implementation_summary": "Schema changes include adding tables for festival capacity and artist availability, modifying existing tables to improve mapping adequacy, and updating configuration logic for scalar parameters and formulas.",
  "or_requirements_analysis": {
    "mapping_gaps_identified": [
      "Festival capacity data is missing",
      "Artist availability on the festival date is missing"
    ],
    "missing_data_requirements": [
      "Festival capacity data",
      "Artist availability on the festival date"
    ],
    "business_configuration_logic_needs": [
      "Festival capacity as a scalar parameter"
    ]
  },
  "schema_adjustment_decisions": {
    "tables_to_delete": [],
    "tables_to_create": [
      {
        "table_name": "artist_availability",
        "purpose": "constraint_bounds",
        "business_meaning": "Represents the availability of artists on the festival date"
      },
      {
        "table_name": "festival_capacity",
        "purpose": "constraint_bounds",
        "business_meaning": "Represents the maximum number of artists that can be selected for the festival"
      }
    ],
    "tables_to_modify": [
      {
        "table_name": "artist",
        "changes": "Add column for availability status",
        "reason": "To incorporate artist availability into the optimization model"
      }
    ]
  },
  "business_configuration_logic_updates": {
    "configuration_parameters": {
      "festival_capacity": {
        "sample_value": "10",
        "data_type": "INTEGER",
        "business_meaning": "Maximum number of artists that can be selected for the festival",
        "optimization_role": "Used as a constraint bound in the optimization model",
        "configuration_type": "scalar_parameter"
      }
    },
    "updates_rationale": "Festival capacity is a single scalar value better suited for configuration logic than a table."
  },
  "implemented_optimization_mapping": {
    "objective_coefficients_mapping": {
      "Weeks_on_Top[i]": "volume.Weeks_on_Top"
    },
    "constraint_bounds_mapping": {
      "festival_capacity": "business_configuration_logic.festival_capacity",
      "availability_constraint[i]": "artist_availability.Availability_Status"
    },
    "decision_variables_mapping": {
      "x[i]": "artist.Artist_ID"
    }
  },
  "data_dictionary": {
    "tables": {
      "artist": {
        "business_purpose": "Stores information about artists",
        "optimization_role": "decision_variables",
        "columns": {
          "Artist_ID": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for each artist",
            "optimization_purpose": "Used as a decision variable in the optimization model",
            "sample_values": "1, 2, 3"
          },
          "Availability_Status": {
            "data_type": "BOOLEAN",
            "business_meaning": "Indicates if the artist is available on the festival date",
            "optimization_purpose": "Used to enforce availability constraint",
            "sample_values": "true, false"
          }
        }
      },
      "artist_availability": {
        "business_purpose": "Stores availability status of artists",
        "optimization_role": "constraint_bounds",
        "columns": {
          "Artist_ID": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for each artist",
            "optimization_purpose": "Links to artist table for availability check",
            "sample_values": "1, 2, 3"
          },
          "Availability_Status": {
            "data_type": "BOOLEAN",
            "business_meaning": "Indicates if the artist is available on the festival date",
            "optimization_purpose": "Used to enforce availability constraint",
            "sample_values": "true, false"
          }
        }
      },
      "festival_capacity": {
        "business_purpose": "Stores the maximum number of artists for the festival",
        "optimization_role": "constraint_bounds",
        "columns": {
          "Capacity": {
            "data_type": "INTEGER",
            "business_meaning": "Maximum number of artists that can be selected",
            "optimization_purpose": "Used as a constraint bound in the optimization model",
            "sample_values": "10"
          }
        }
      }
    }
  },
  "data_mapping": {
    "objective_sources": [
      "volume.Weeks_on_Top"
    ],
    "constraint_sources": [
      "artist_availability.Availability_Status",
      "business_configuration_logic.festival_capacity"
    ],
    "sample_data_rows": {
      "artist": 3,
      "artist_availability": 3,
      "festival_capacity": 1
    }
  },
  "validation": {
    "schema_complete": true,
    "data_consistent": true,
    "math_traceable": true,
    "business_aligned": true,
    "follows_db_standards": true
  }
}
