Iteration 1 - DATA_ENGINEER
Sequence: 2
Timestamp: 2025-07-27 22:02:29

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": "cinema",
  "iteration": 0,
  "business_context": "A cinema chain wants to maximize its revenue by optimally scheduling films across its cinemas, considering the capacity of each cinema and the number of show times available per day.",
  "optimization_problem_description": "The goal is to maximize the total revenue from film screenings across all cinemas by deciding how many times each film should be shown in each cinema, subject to constraints on cinema capacity and available show times.",
  "optimization_formulation": {
    "objective": "maximize total_revenue = sum(schedule[Cinema_ID, Film_ID] * schedule[Price])",
    "decision_variables": "schedule[Cinema_ID, Film_ID] - number of times a film is shown in a cinema (integer)",
    "constraints": [
      "sum(schedule[Cinema_ID, Film_ID] * schedule[Show_times_per_day]) <= cinema[Capacity] for each Cinema_ID",
      "schedule[Cinema_ID, Film_ID] >= 0 for each Cinema_ID, Film_ID"
    ]
  },
  "current_optimization_to_schema_mapping": {
    "objective_coefficients": {
      "schedule[Price]": {
        "currently_mapped_to": "schedule.Price",
        "mapping_adequacy": "good",
        "description": "Price of a ticket for a specific film in a specific cinema"
      }
    },
    "constraint_bounds": {
      "cinema[Capacity]": {
        "currently_mapped_to": "cinema.Capacity",
        "mapping_adequacy": "good",
        "description": "Maximum number of people that can be accommodated in a cinema"
      },
      "schedule[Show_times_per_day]": {
        "currently_mapped_to": "schedule.Show_times_per_day",
        "mapping_adequacy": "good",
        "description": "Number of show times available per day for a specific film in a specific cinema"
      }
    },
    "decision_variables": {
      "schedule[Cinema_ID, Film_ID]": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "Number of times a film is shown in a cinema",
        "variable_type": "integer"
      }
    }
  },
  "missing_optimization_requirements": [
    "Data on the maximum number of screenings possible per day for each cinema",
    "Historical data on demand for each film to better estimate potential revenue"
  ],
  "iteration_status": {
    "complete": false,
    "confidence": "medium",
    "next_focus": "Refine the estimation of demand and incorporate additional constraints based on historical data"
  }
}





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

JSON STRUCTURE REQUIRED:

{
  "database_id": "cinema",
  "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": "cinema",
  "iteration": 1,
  "implementation_summary": "Schema changes include adding a new table for decision variables, modifying existing tables to fill mapping gaps, and updating configuration logic for scalar parameters and formulas.",
  "or_requirements_analysis": {
    "mapping_gaps_identified": [
      "schedule[Cinema_ID, Film_ID] missing mapping for decision variables"
    ],
    "missing_data_requirements": [
      "Maximum number of screenings possible per day for each cinema",
      "Historical data on demand for each film"
    ],
    "business_configuration_logic_needs": [
      "Maximum screenings per day as scalar parameter",
      "Demand estimation formula"
    ]
  },
  "schema_adjustment_decisions": {
    "tables_to_delete": [],
    "tables_to_create": [
      {
        "table_name": "film_schedule",
        "purpose": "decision_variables",
        "business_meaning": "Represents the number of times each film is shown in each cinema"
      }
    ],
    "tables_to_modify": [
      {
        "table_name": "cinema",
        "changes": "Add column for maximum screenings per day",
        "reason": "To address missing data requirement for maximum screenings"
      }
    ]
  },
  "business_configuration_logic_updates": {
    "configuration_parameters": {
      "max_screenings_per_day": {
        "sample_value": "5",
        "data_type": "INTEGER",
        "business_meaning": "Maximum number of screenings possible per day for each cinema",
        "optimization_role": "Constraint parameter for scheduling",
        "configuration_type": "scalar_parameter"
      },
      "demand_estimation": {
        "formula_expression": "historical_demand * adjustment_factor",
        "data_type": "STRING",
        "business_meaning": "Estimates demand for each film based on historical data",
        "optimization_role": "Used to refine revenue estimation",
        "configuration_type": "business_logic_formula"
      }
    },
    "updates_rationale": "Parameters like maximum screenings and demand estimation are better managed as configuration logic due to their scalar nature and formulaic complexity."
  },
  "implemented_optimization_mapping": {
    "objective_coefficients_mapping": {
      "schedule[Price]": "schedule.Price"
    },
    "constraint_bounds_mapping": {
      "cinema[Capacity]": "cinema.Capacity",
      "schedule[Show_times_per_day]": "schedule.Show_times_per_day"
    },
    "decision_variables_mapping": {
      "schedule[Cinema_ID, Film_ID]": "film_schedule.Cinema_ID_Film_ID"
    }
  },
  "data_dictionary": {
    "tables": {
      "cinema": {
        "business_purpose": "Stores information about each cinema",
        "optimization_role": "constraint_bounds",
        "columns": {
          "Cinema_ID": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for each cinema",
            "optimization_purpose": "Used to link cinema data with schedules",
            "sample_values": "1, 2, 3"
          },
          "Capacity": {
            "data_type": "INTEGER",
            "business_meaning": "Maximum number of people that can be accommodated",
            "optimization_purpose": "Constraint for scheduling",
            "sample_values": "100, 150, 200"
          },
          "Max_Screenings_Per_Day": {
            "data_type": "INTEGER",
            "business_meaning": "Maximum screenings possible per day",
            "optimization_purpose": "Constraint for scheduling",
            "sample_values": "5, 6, 7"
          }
        }
      },
      "film_schedule": {
        "business_purpose": "Stores the number of times each film is shown in each cinema",
        "optimization_role": "decision_variables",
        "columns": {
          "Cinema_ID_Film_ID": {
            "data_type": "INTEGER",
            "business_meaning": "Composite key for cinema and film",
            "optimization_purpose": "Decision variable for scheduling",
            "sample_values": "101, 102, 103"
          },
          "Show_Times": {
            "data_type": "INTEGER",
            "business_meaning": "Number of times a film is shown",
            "optimization_purpose": "Decision variable for scheduling",
            "sample_values": "2, 3, 4"
          }
        }
      }
    }
  },
  "data_mapping": {
    "objective_sources": [
      "schedule.Price"
    ],
    "constraint_sources": [
      "cinema.Capacity",
      "schedule.Show_times_per_day"
    ],
    "sample_data_rows": {
      "cinema": 3,
      "film_schedule": 5
    }
  },
  "validation": {
    "schema_complete": true,
    "data_consistent": true,
    "math_traceable": true,
    "business_aligned": true,
    "follows_db_standards": true
  }
}
