Iteration 1 - DATA_ENGINEER
Sequence: 2
Timestamp: 2025-07-25 22:45:19

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": "music_4",
  "iteration": 0,
  "business_context": "A music festival organizer wants to maximize the total number of weeks that songs featured in the festival have spent on top charts, while ensuring that the festival includes a diverse set of artists and respects budget constraints on the number of songs per artist.",
  "optimization_problem_description": "The objective is to maximize the total weeks on top charts for songs selected for the festival. Constraints include limiting the number of songs per artist, ensuring a minimum number of artists are represented, and respecting the festival's capacity for the total number of songs.",
  "optimization_formulation": {
    "objective": "maximize \u2211(Weeks_on_Top \u00d7 x_song)",
    "decision_variables": "x_song: binary variable indicating whether a song is selected for the festival (1 if selected, 0 otherwise)",
    "constraints": [
      "\u2211(x_song) \u2264 Total_Song_Capacity (festival's maximum number of songs)",
      "\u2211(x_song for each artist) \u2264 Max_Songs_Per_Artist (maximum songs per artist)",
      "\u2211(x_song for each artist) \u2265 Min_Artists (minimum number of artists to be represented)",
      "x_song \u2208 {0, 1} (binary decision variable)"
    ]
  },
  "current_optimization_to_schema_mapping": {
    "objective_coefficients": {
      "Weeks_on_Top[song]": {
        "currently_mapped_to": "volume.Weeks_on_Top",
        "mapping_adequacy": "good",
        "description": "Number of weeks the song has been on top charts"
      }
    },
    "constraint_bounds": {
      "Total_Song_Capacity": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "Maximum number of songs the festival can include"
      },
      "Max_Songs_Per_Artist[artist]": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "Maximum number of songs per artist allowed in the festival"
      },
      "Min_Artists": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "Minimum number of artists to be represented in the festival"
      }
    },
    "decision_variables": {
      "x_song[song]": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "Binary decision variable indicating whether a song is selected for the festival",
        "variable_type": "binary"
      }
    }
  },
  "missing_optimization_requirements": [
    "Total_Song_Capacity",
    "Max_Songs_Per_Artist",
    "Min_Artists"
  ],
  "iteration_status": {
    "complete": false,
    "confidence": "medium",
    "next_focus": "Define and map the missing constraint bounds and decision variables to the schema or business configuration logic."
  }
}





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 creating new tables for decision variables and constraint bounds, and updating business configuration logic for scalar parameters and formulas.",
  "or_requirements_analysis": {
    "mapping_gaps_identified": [
      "Total_Song_Capacity",
      "Max_Songs_Per_Artist",
      "Min_Artists",
      "x_song[song]"
    ],
    "missing_data_requirements": [
      "Total_Song_Capacity",
      "Max_Songs_Per_Artist",
      "Min_Artists",
      "x_song[song]"
    ],
    "business_configuration_logic_needs": [
      "Total_Song_Capacity",
      "Max_Songs_Per_Artist",
      "Min_Artists"
    ]
  },
  "schema_adjustment_decisions": {
    "tables_to_delete": [],
    "tables_to_create": [
      {
        "table_name": "song_selection",
        "purpose": "decision_variables",
        "business_meaning": "Binary decision variable indicating whether a song is selected for the festival"
      }
    ],
    "tables_to_modify": []
  },
  "business_configuration_logic_updates": {
    "configuration_parameters": {
      "Total_Song_Capacity": {
        "sample_value": 50,
        "data_type": "INTEGER",
        "business_meaning": "Maximum number of songs the festival can include",
        "optimization_role": "Constraint bound for total song capacity",
        "configuration_type": "scalar_parameter"
      },
      "Max_Songs_Per_Artist": {
        "sample_value": 3,
        "data_type": "INTEGER",
        "business_meaning": "Maximum number of songs per artist allowed in the festival",
        "optimization_role": "Constraint bound for maximum songs per artist",
        "configuration_type": "scalar_parameter"
      },
      "Min_Artists": {
        "sample_value": 10,
        "data_type": "INTEGER",
        "business_meaning": "Minimum number of artists to be represented in the festival",
        "optimization_role": "Constraint bound for minimum number of artists",
        "configuration_type": "scalar_parameter"
      }
    },
    "updates_rationale": "These parameters are better suited for configuration logic as they are scalar values and do not require table storage."
  },
  "implemented_optimization_mapping": {
    "objective_coefficients_mapping": {
      "Weeks_on_Top[song]": "volume.Weeks_on_Top"
    },
    "constraint_bounds_mapping": {
      "Total_Song_Capacity": "business_configuration_logic.Total_Song_Capacity",
      "Max_Songs_Per_Artist[artist]": "business_configuration_logic.Max_Songs_Per_Artist",
      "Min_Artists": "business_configuration_logic.Min_Artists"
    },
    "decision_variables_mapping": {
      "x_song[song]": "song_selection.x_song"
    }
  },
  "data_dictionary": {
    "tables": {
      "song_selection": {
        "business_purpose": "Binary decision variable indicating whether a song is selected for the festival",
        "optimization_role": "decision_variables",
        "columns": {
          "x_song": {
            "data_type": "BOOLEAN",
            "business_meaning": "Binary decision variable indicating whether a song is selected for the festival",
            "optimization_purpose": "Decision variable in optimization formulation",
            "sample_values": [
              0,
              1
            ]
          }
        }
      },
      "volume": {
        "business_purpose": "Number of weeks the song has been on top charts",
        "optimization_role": "objective_coefficients",
        "columns": {
          "Weeks_on_Top": {
            "data_type": "INTEGER",
            "business_meaning": "Number of weeks the song has been on top charts",
            "optimization_purpose": "Coefficient in objective function",
            "sample_values": [
              5,
              10,
              15
            ]
          }
        }
      }
    }
  },
  "data_mapping": {
    "objective_sources": [
      "volume.Weeks_on_Top"
    ],
    "constraint_sources": [
      "business_configuration_logic.Total_Song_Capacity",
      "business_configuration_logic.Max_Songs_Per_Artist",
      "business_configuration_logic.Min_Artists"
    ],
    "sample_data_rows": {
      "song_selection": 3,
      "volume": 5
    }
  },
  "validation": {
    "schema_complete": true,
    "data_consistent": true,
    "math_traceable": true,
    "business_aligned": true,
    "follows_db_standards": true
  }
}
