Iteration 1 - DATA_ENGINEER
Sequence: 2
Timestamp: 2025-07-25 22:37:12

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": "university_basketball",
  "iteration": 0,
  "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": "The objective is to 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: 1 if team is selected, 0 otherwise)",
    "constraints": [
      "\u2211(Team_Selection) = Total_Teams_Required",
      "\u2211(Team_Selection \u00d7 Conference_Indicator) \u2265 Min_Teams_Per_Conference",
      "\u2211(Team_Selection \u00d7 Location_Indicator) \u2264 Max_Teams_Per_Location"
    ]
  },
  "current_optimization_to_schema_mapping": {
    "objective_coefficients": {
      "All_Games_Percent[Team_ID]": {
        "currently_mapped_to": "basketball_match.All_Games_Percent",
        "mapping_adequacy": "good",
        "description": "Win percentage of each team"
      }
    },
    "constraint_bounds": {
      "Total_Teams_Required": {
        "currently_mapped_to": "business_configuration_logic.key",
        "mapping_adequacy": "missing",
        "description": "Total number of teams to be selected"
      },
      "Min_Teams_Per_Conference": {
        "currently_mapped_to": "business_configuration_logic.key",
        "mapping_adequacy": "missing",
        "description": "Minimum number of teams required from each conference"
      },
      "Max_Teams_Per_Location": {
        "currently_mapped_to": "business_configuration_logic.key",
        "mapping_adequacy": "missing",
        "description": "Maximum number of teams allowed from the same location"
      }
    },
    "decision_variables": {
      "Team_Selection[Team_ID]": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "Binary decision variable indicating whether a team is selected",
        "variable_type": "binary"
      }
    }
  },
  "missing_optimization_requirements": [
    "Total_Teams_Required",
    "Min_Teams_Per_Conference",
    "Max_Teams_Per_Location",
    "Conference_Indicator",
    "Location_Indicator"
  ],
  "iteration_status": {
    "complete": false,
    "confidence": "medium",
    "next_focus": "Define and map additional parameters such as Total_Teams_Required, Min_Teams_Per_Conference, and Max_Teams_Per_Location to the business configuration logic."
  }
}





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

JSON STRUCTURE REQUIRED:

{
  "database_id": "university_basketball",
  "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": "university_basketball",
  "iteration": 1,
  "implementation_summary": "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.",
  "or_requirements_analysis": {
    "mapping_gaps_identified": [
      "Total_Teams_Required",
      "Min_Teams_Per_Conference",
      "Max_Teams_Per_Location",
      "Conference_Indicator",
      "Location_Indicator"
    ],
    "missing_data_requirements": [
      "Total_Teams_Required",
      "Min_Teams_Per_Conference",
      "Max_Teams_Per_Location",
      "Conference_Indicator",
      "Location_Indicator"
    ],
    "business_configuration_logic_needs": [
      "Total_Teams_Required",
      "Min_Teams_Per_Conference",
      "Max_Teams_Per_Location"
    ]
  },
  "schema_adjustment_decisions": {
    "tables_to_delete": [],
    "tables_to_create": [
      {
        "table_name": "team_selection",
        "purpose": "decision_variables",
        "business_meaning": "Binary decision variable indicating whether a team is selected for the tournament"
      },
      {
        "table_name": "conference_indicator",
        "purpose": "constraint_bounds",
        "business_meaning": "Indicator of which conference each team belongs to"
      },
      {
        "table_name": "location_indicator",
        "purpose": "constraint_bounds",
        "business_meaning": "Indicator of the location of each team"
      }
    ],
    "tables_to_modify": []
  },
  "business_configuration_logic_updates": {
    "configuration_parameters": {
      "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"
      }
    },
    "updates_rationale": "These parameters are scalar values that define the constraints of the optimization problem and are better suited for configuration logic than tables."
  },
  "implemented_optimization_mapping": {
    "objective_coefficients_mapping": {
      "All_Games_Percent[Team_ID]": "basketball_match.All_Games_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": {
      "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": [
      "basketball_match.All_Games_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": {
      "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
  }
}
