Iteration 2 - DATA_ENGINEER
Sequence: 4
Timestamp: 2025-07-25 22:37:12

Prompt:
You are a senior database architect implementing schema modifications for iteration 2. 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 2):
{
  "database_id": "university_basketball",
  "iteration": 1,
  "business_context": "Optimize the selection of basketball teams for a tournament to maximize the overall win percentage while respecting constraints on team diversity and geographical distribution.",
  "optimization_problem_description": "Maximize the total win percentage of selected teams for a tournament. Constraints include selecting a specific number of teams, ensuring a minimum number of teams from different conferences, and limiting the number of teams from the same location.",
  "optimization_formulation": {
    "objective": "maximize \u2211(All_Games_Percent \u00d7 Team_Selection)",
    "decision_variables": "Team_Selection[Team_ID] (binary)",
    "constraints": [
      "\u2211(Team_Selection[Team_ID]) = Total_Teams_Required",
      "\u2211(Conference_Indicator[Team_ID] == 'Conference_X' ? Team_Selection[Team_ID] : 0) \u2265 Min_Teams_Per_Conference for each Conference_X",
      "\u2211(Location_Indicator[Team_ID] == 'Location_Y' ? Team_Selection[Team_ID] : 0) \u2264 Max_Teams_Per_Location for each Location_Y"
    ]
  },
  "current_optimization_to_schema_mapping": {
    "objective_coefficients": {
      "All_Games_Percent[Team_ID]": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "Win percentage of each team"
      }
    },
    "constraint_bounds": {
      "Total_Teams_Required": {
        "currently_mapped_to": "business_configuration_logic.Total_Teams_Required",
        "mapping_adequacy": "good",
        "description": "Total number of teams to be selected"
      },
      "Min_Teams_Per_Conference": {
        "currently_mapped_to": "business_configuration_logic.Min_Teams_Per_Conference",
        "mapping_adequacy": "good",
        "description": "Minimum number of teams required from each conference"
      },
      "Max_Teams_Per_Location": {
        "currently_mapped_to": "business_configuration_logic.Max_Teams_Per_Location",
        "mapping_adequacy": "good",
        "description": "Maximum number of teams allowed from the same location"
      }
    },
    "decision_variables": {
      "Team_Selection[Team_ID]": {
        "currently_mapped_to": "team_selection.Team_Selection",
        "mapping_adequacy": "good",
        "description": "Binary decision variable indicating whether a team is selected",
        "variable_type": "binary"
      }
    }
  },
  "missing_optimization_requirements": [
    "All_Games_Percent[Team_ID]"
  ],
  "iteration_status": {
    "complete": false,
    "confidence": "medium",
    "next_focus": "Map the win percentage data for each team to complete the objective function."
  }
}


CURRENT DATABASE SCHEMA:
```sql
-- Iteration 1 Database Schema
-- Objective: Schema changes include creating new tables for decision variables and indicators, updating business configuration logic with scalar parameters and formulas, and ensuring all optimization requirements are mapped correctly.

CREATE TABLE team_selection (
  Team_ID INTEGER,
  Team_Selection BOOLEAN
);

CREATE TABLE conference_indicator (
  Team_ID INTEGER,
  Conference_Indicator STRING
);

CREATE TABLE location_indicator (
  Team_ID INTEGER,
  Location_Indicator STRING
);


```


CURRENT DATA DICTIONARY:
{
  "tables": {
    "team_selection": {
      "business_purpose": "Binary decision variable indicating whether a team is selected for the tournament",
      "optimization_role": "decision_variables",
      "columns": {
        "Team_ID": {
          "data_type": "INTEGER",
          "business_meaning": "Unique identifier for each team",
          "optimization_purpose": "Index for decision variable",
          "sample_values": [
            1,
            2,
            3
          ]
        },
        "Team_Selection": {
          "data_type": "BOOLEAN",
          "business_meaning": "Binary indicator of team selection",
          "optimization_purpose": "Decision variable in optimization model",
          "sample_values": [
            true,
            false,
            true
          ]
        }
      }
    },
    "conference_indicator": {
      "business_purpose": "Indicator of which conference each team belongs to",
      "optimization_role": "constraint_bounds",
      "columns": {
        "Team_ID": {
          "data_type": "INTEGER",
          "business_meaning": "Unique identifier for each team",
          "optimization_purpose": "Index for conference indicator",
          "sample_values": [
            1,
            2,
            3
          ]
        },
        "Conference_Indicator": {
          "data_type": "STRING",
          "business_meaning": "Conference affiliation of the team",
          "optimization_purpose": "Constraint bound for conference diversity",
          "sample_values": [
            "East",
            "West",
            "South"
          ]
        }
      }
    },
    "location_indicator": {
      "business_purpose": "Indicator of the location of each team",
      "optimization_role": "constraint_bounds",
      "columns": {
        "Team_ID": {
          "data_type": "INTEGER",
          "business_meaning": "Unique identifier for each team",
          "optimization_purpose": "Index for location indicator",
          "sample_values": [
            1,
            2,
            3
          ]
        },
        "Location_Indicator": {
          "data_type": "STRING",
          "business_meaning": "Location of the team",
          "optimization_purpose": "Constraint bound for geographical distribution",
          "sample_values": [
            "New York",
            "Los Angeles",
            "Chicago"
          ]
        }
      }
    }
  }
}


CURRENT BUSINESS CONFIGURATION LOGIC:
{
  "Total_Teams_Required": {
    "sample_value": 10,
    "data_type": "INTEGER",
    "business_meaning": "Total number of teams to be selected for the tournament",
    "optimization_role": "Constraint bound for total team selection",
    "configuration_type": "scalar_parameter"
  },
  "Min_Teams_Per_Conference": {
    "sample_value": 2,
    "data_type": "INTEGER",
    "business_meaning": "Minimum number of teams required from each conference",
    "optimization_role": "Constraint bound for conference diversity",
    "configuration_type": "scalar_parameter"
  },
  "Max_Teams_Per_Location": {
    "sample_value": 3,
    "data_type": "INTEGER",
    "business_meaning": "Maximum number of teams allowed from the same location",
    "optimization_role": "Constraint bound for geographical distribution",
    "configuration_type": "scalar_parameter"
  }
}


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

JSON STRUCTURE REQUIRED:

{
  "database_id": "university_basketball",
  "iteration": 2,
  "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": "university_basketball",
  "iteration": 2,
  "implementation_summary": "Added a new table for win percentage data to complete the objective function mapping. Updated business configuration logic to include a formula for calculating win percentage. Ensured all optimization requirements are mapped correctly.",
  "or_requirements_analysis": {
    "mapping_gaps_identified": [
      "All_Games_Percent[Team_ID] is missing in the schema"
    ],
    "missing_data_requirements": [
      "All_Games_Percent[Team_ID]"
    ],
    "business_configuration_logic_needs": [
      "Win percentage calculation formula"
    ]
  },
  "schema_adjustment_decisions": {
    "tables_to_delete": [],
    "tables_to_create": [
      {
        "table_name": "win_percentage",
        "purpose": "objective_coefficients",
        "business_meaning": "Win percentage of each team for the tournament selection optimization"
      }
    ],
    "tables_to_modify": []
  },
  "business_configuration_logic_updates": {
    "configuration_parameters": {
      "Win_Percentage_Formula": {
        "formula_expression": "(Total_Wins / Total_Games) * 100",
        "data_type": "STRING",
        "business_meaning": "Formula to calculate the win percentage of a team",
        "optimization_role": "Used to compute the objective coefficient for each team",
        "configuration_type": "business_logic_formula"
      }
    },
    "updates_rationale": "The win percentage formula is better suited for configuration logic as it is a calculation rather than a static value."
  },
  "implemented_optimization_mapping": {
    "objective_coefficients_mapping": {
      "All_Games_Percent[Team_ID]": "win_percentage.Win_Percent"
    },
    "constraint_bounds_mapping": {
      "Total_Teams_Required": "business_configuration_logic.Total_Teams_Required",
      "Min_Teams_Per_Conference": "business_configuration_logic.Min_Teams_Per_Conference",
      "Max_Teams_Per_Location": "business_configuration_logic.Max_Teams_Per_Location"
    },
    "decision_variables_mapping": {
      "Team_Selection[Team_ID]": "team_selection.Team_Selection"
    }
  },
  "data_dictionary": {
    "tables": {
      "win_percentage": {
        "business_purpose": "Win percentage of each team for the tournament selection optimization",
        "optimization_role": "objective_coefficients",
        "columns": {
          "Team_ID": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for each team",
            "optimization_purpose": "Index for win percentage",
            "sample_values": [
              1,
              2,
              3
            ]
          },
          "Win_Percent": {
            "data_type": "FLOAT",
            "business_meaning": "Win percentage of the team",
            "optimization_purpose": "Objective coefficient in optimization model",
            "sample_values": [
              75.5,
              80.0,
              65.3
            ]
          }
        }
      },
      "team_selection": {
        "business_purpose": "Binary decision variable indicating whether a team is selected for the tournament",
        "optimization_role": "decision_variables",
        "columns": {
          "Team_ID": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for each team",
            "optimization_purpose": "Index for decision variable",
            "sample_values": [
              1,
              2,
              3
            ]
          },
          "Team_Selection": {
            "data_type": "BOOLEAN",
            "business_meaning": "Binary indicator of team selection",
            "optimization_purpose": "Decision variable in optimization model",
            "sample_values": [
              true,
              false,
              true
            ]
          }
        }
      },
      "conference_indicator": {
        "business_purpose": "Indicator of which conference each team belongs to",
        "optimization_role": "constraint_bounds",
        "columns": {
          "Team_ID": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for each team",
            "optimization_purpose": "Index for conference indicator",
            "sample_values": [
              1,
              2,
              3
            ]
          },
          "Conference_Indicator": {
            "data_type": "STRING",
            "business_meaning": "Conference affiliation of the team",
            "optimization_purpose": "Constraint bound for conference diversity",
            "sample_values": [
              "East",
              "West",
              "South"
            ]
          }
        }
      },
      "location_indicator": {
        "business_purpose": "Indicator of the location of each team",
        "optimization_role": "constraint_bounds",
        "columns": {
          "Team_ID": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for each team",
            "optimization_purpose": "Index for location indicator",
            "sample_values": [
              1,
              2,
              3
            ]
          },
          "Location_Indicator": {
            "data_type": "STRING",
            "business_meaning": "Location of the team",
            "optimization_purpose": "Constraint bound for geographical distribution",
            "sample_values": [
              "New York",
              "Los Angeles",
              "Chicago"
            ]
          }
        }
      }
    }
  },
  "data_mapping": {
    "objective_sources": [
      "win_percentage.Win_Percent"
    ],
    "constraint_sources": [
      "business_configuration_logic.Total_Teams_Required",
      "business_configuration_logic.Min_Teams_Per_Conference",
      "business_configuration_logic.Max_Teams_Per_Location"
    ],
    "sample_data_rows": {
      "win_percentage": 3,
      "team_selection": 3,
      "conference_indicator": 3,
      "location_indicator": 3
    }
  },
  "validation": {
    "schema_complete": true,
    "data_consistent": true,
    "math_traceable": true,
    "business_aligned": true,
    "follows_db_standards": true
  }
}
