Iteration 1 - DATA_ENGINEER
Sequence: 2
Timestamp: 2025-07-25 22:35:08

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": "movie_1",
  "iteration": 0,
  "business_context": "A movie streaming platform wants to maximize viewer satisfaction by recommending movies based on ratings. The platform aims to allocate a limited number of recommendations to movies such that the total satisfaction (sum of stars) is maximized, while ensuring that no movie is recommended more than a certain number of times and no reviewer is overloaded with recommendations.",
  "optimization_problem_description": "The platform needs to decide how many times each movie should be recommended to maximize the total satisfaction (sum of stars) from the ratings. Constraints include limiting the number of recommendations per movie and ensuring that no reviewer receives too many recommendations.",
  "optimization_formulation": {
    "objective": "maximize \u2211(stars[i,j] * x[i,j]) where x[i,j] is the number of times movie j is recommended to reviewer i",
    "decision_variables": "x[i,j] = number of times movie j is recommended to reviewer i (integer)",
    "constraints": [
      "\u2211(x[i,j]) \u2264 max_recommendations_per_movie[j] for each movie j",
      "\u2211(x[i,j]) \u2264 max_recommendations_per_reviewer[i] for each reviewer i",
      "x[i,j] \u2265 0 for all i, j"
    ]
  },
  "current_optimization_to_schema_mapping": {
    "objective_coefficients": {
      "stars[i,j]": {
        "currently_mapped_to": "Rating.stars",
        "mapping_adequacy": "good",
        "description": "The rating stars given by reviewer i to movie j"
      }
    },
    "constraint_bounds": {
      "max_recommendations_per_movie[j]": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "Maximum number of recommendations allowed for movie j"
      },
      "max_recommendations_per_reviewer[i]": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "Maximum number of recommendations allowed for reviewer i"
      }
    },
    "decision_variables": {
      "x[i,j]": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "Number of times movie j is recommended to reviewer i",
        "variable_type": "integer"
      }
    }
  },
  "missing_optimization_requirements": [
    "max_recommendations_per_movie[j]",
    "max_recommendations_per_reviewer[i]"
  ],
  "iteration_status": {
    "complete": false,
    "confidence": "medium",
    "next_focus": "Define and map the missing constraint bounds for max_recommendations_per_movie and max_recommendations_per_reviewer"
  }
}





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

JSON STRUCTURE REQUIRED:

{
  "database_id": "movie_1",
  "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": "movie_1",
  "iteration": 1,
  "implementation_summary": "Schema changes include creating tables for missing constraint bounds and decision variables, and updating business configuration logic for scalar parameters. Data dictionary and mapping are updated to reflect these changes.",
  "or_requirements_analysis": {
    "mapping_gaps_identified": [
      "max_recommendations_per_movie[j]",
      "max_recommendations_per_reviewer[i]",
      "x[i,j]"
    ],
    "missing_data_requirements": [
      "max_recommendations_per_movie[j]",
      "max_recommendations_per_reviewer[i]"
    ],
    "business_configuration_logic_needs": [
      "max_recommendations_per_movie[j]",
      "max_recommendations_per_reviewer[i]"
    ]
  },
  "schema_adjustment_decisions": {
    "tables_to_delete": [],
    "tables_to_create": [
      {
        "table_name": "MovieRecommendationLimits",
        "purpose": "constraint_bounds",
        "business_meaning": "Maximum number of recommendations allowed for each movie"
      },
      {
        "table_name": "ReviewerRecommendationLimits",
        "purpose": "constraint_bounds",
        "business_meaning": "Maximum number of recommendations allowed for each reviewer"
      },
      {
        "table_name": "RecommendationAssignments",
        "purpose": "decision_variables",
        "business_meaning": "Number of times each movie is recommended to each reviewer"
      }
    ],
    "tables_to_modify": []
  },
  "business_configuration_logic_updates": {
    "configuration_parameters": {
      "max_recommendations_per_movie": {
        "sample_value": 5,
        "data_type": "INTEGER",
        "business_meaning": "Maximum number of recommendations allowed for a movie",
        "optimization_role": "Constraint bound for movie recommendations",
        "configuration_type": "scalar_parameter"
      },
      "max_recommendations_per_reviewer": {
        "sample_value": 3,
        "data_type": "INTEGER",
        "business_meaning": "Maximum number of recommendations allowed for a reviewer",
        "optimization_role": "Constraint bound for reviewer recommendations",
        "configuration_type": "scalar_parameter"
      }
    },
    "updates_rationale": "These parameters are better suited for configuration logic as they are scalar values and do not require multiple rows in a table."
  },
  "implemented_optimization_mapping": {
    "objective_coefficients_mapping": {
      "stars[i,j]": "Rating.stars"
    },
    "constraint_bounds_mapping": {
      "max_recommendations_per_movie[j]": "business_configuration_logic.max_recommendations_per_movie",
      "max_recommendations_per_reviewer[i]": "business_configuration_logic.max_recommendations_per_reviewer"
    },
    "decision_variables_mapping": {
      "x[i,j]": "RecommendationAssignments.recommendation_count"
    }
  },
  "data_dictionary": {
    "tables": {
      "MovieRecommendationLimits": {
        "business_purpose": "Maximum number of recommendations allowed for each movie",
        "optimization_role": "constraint_bounds",
        "columns": {
          "movie_id": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for the movie",
            "optimization_purpose": "Identifies the movie for which the recommendation limit applies",
            "sample_values": "1, 2, 3"
          },
          "max_recommendations": {
            "data_type": "INTEGER",
            "business_meaning": "Maximum number of recommendations allowed for the movie",
            "optimization_purpose": "Constraint bound for movie recommendations",
            "sample_values": "5, 5, 5"
          }
        }
      },
      "ReviewerRecommendationLimits": {
        "business_purpose": "Maximum number of recommendations allowed for each reviewer",
        "optimization_role": "constraint_bounds",
        "columns": {
          "reviewer_id": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for the reviewer",
            "optimization_purpose": "Identifies the reviewer for which the recommendation limit applies",
            "sample_values": "1, 2, 3"
          },
          "max_recommendations": {
            "data_type": "INTEGER",
            "business_meaning": "Maximum number of recommendations allowed for the reviewer",
            "optimization_purpose": "Constraint bound for reviewer recommendations",
            "sample_values": "3, 3, 3"
          }
        }
      },
      "RecommendationAssignments": {
        "business_purpose": "Number of times each movie is recommended to each reviewer",
        "optimization_role": "decision_variables",
        "columns": {
          "reviewer_id": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for the reviewer",
            "optimization_purpose": "Identifies the reviewer receiving the recommendation",
            "sample_values": "1, 2, 3"
          },
          "movie_id": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for the movie",
            "optimization_purpose": "Identifies the movie being recommended",
            "sample_values": "1, 2, 3"
          },
          "recommendation_count": {
            "data_type": "INTEGER",
            "business_meaning": "Number of times the movie is recommended to the reviewer",
            "optimization_purpose": "Decision variable for recommendation assignments",
            "sample_values": "1, 2, 3"
          }
        }
      }
    }
  },
  "data_mapping": {
    "objective_sources": [
      "Rating.stars"
    ],
    "constraint_sources": [
      "business_configuration_logic.max_recommendations_per_movie",
      "business_configuration_logic.max_recommendations_per_reviewer"
    ],
    "sample_data_rows": {
      "MovieRecommendationLimits": 3,
      "ReviewerRecommendationLimits": 3,
      "RecommendationAssignments": 3
    }
  },
  "validation": {
    "schema_complete": true,
    "data_consistent": true,
    "math_traceable": true,
    "business_aligned": true,
    "follows_db_standards": true
  }
}
