Iteration 1 - DATA_ENGINEER
Sequence: 2
Timestamp: 2025-07-28 00:25:08

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 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": "protein_institute",
  "iteration": 0,
  "business_context": "The protein institute aims to optimize the allocation of research resources across different institutions to maximize the total sequence identity to human proteins, given constraints on building capacities and institutional enrollments.",
  "optimization_problem_description": "Optimize the allocation of research resources to maximize the total sequence identity to human proteins across institutions, subject to constraints on building capacities and institutional enrollments.",
  "optimization_formulation": {
    "objective": "maximize total_sequence_identity = \u2211(sequence_identity_coefficient[i] * allocation[i])",
    "decision_variables": "allocation[i]: the amount of resources allocated to institution i (continuous)",
    "constraints": [
      "\u2211(allocation[i]) \u2264 total_available_resources",
      "allocation[i] \u2264 building_capacity[i] for all i",
      "allocation[i] \u2264 enrollment[i] for all i"
    ]
  },
  "current_optimization_to_schema_mapping": {
    "objective_coefficients": {
      "sequence_identity_coefficient[i]": {
        "currently_mapped_to": "protein.sequence_identity_to_human_protein",
        "mapping_adequacy": "partial",
        "description": "Represents the sequence identity to human proteins for institution i"
      }
    },
    "constraint_bounds": {
      "total_available_resources": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "Total resources available for allocation"
      },
      "building_capacity[i]": {
        "currently_mapped_to": "building.Floors",
        "mapping_adequacy": "partial",
        "description": "Represents the capacity of the building associated with institution i"
      },
      "enrollment[i]": {
        "currently_mapped_to": "Institution.Enrollment",
        "mapping_adequacy": "good",
        "description": "Represents the enrollment capacity of institution i"
      }
    },
    "decision_variables": {
      "allocation[i]": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "Represents the amount of resources allocated to institution i",
        "variable_type": "continuous"
      }
    }
  },
  "missing_optimization_requirements": [
    "Total available resources for allocation",
    "Mapping of sequence identity to a numerical coefficient",
    "Explicit mapping of building capacity to a numerical value"
  ],
  "iteration_status": {
    "complete": false,
    "confidence": "medium",
    "next_focus": "Refine mapping of sequence identity and building capacity to numerical coefficients"
  }
}





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

JSON STRUCTURE REQUIRED:

{
  "database_id": "protein_institute",
  "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": "protein_institute",
  "iteration": 1,
  "implementation_summary": "Schema changes include creating new tables for missing optimization data, modifying existing tables to improve mapping adequacy, and updating business configuration logic for scalar parameters and formulas.",
  "or_requirements_analysis": {
    "mapping_gaps_identified": [
      "sequence_identity_coefficient[i] mapping is partial",
      "total_available_resources mapping is missing",
      "building_capacity[i] mapping is partial"
    ],
    "missing_data_requirements": [
      "Total available resources for allocation",
      "Mapping of sequence identity to a numerical coefficient",
      "Explicit mapping of building capacity to a numerical value"
    ],
    "business_configuration_logic_needs": [
      "total_available_resources as scalar_parameter",
      "sequence_identity_coefficient[i] as scalar_parameter",
      "building_capacity[i] as scalar_parameter"
    ]
  },
  "schema_adjustment_decisions": {
    "tables_to_delete": [],
    "tables_to_create": [
      {
        "table_name": "ResourceAllocation",
        "purpose": "decision_variables",
        "business_meaning": "Represents the allocation of resources to each institution"
      }
    ],
    "tables_to_modify": [
      {
        "table_name": "protein",
        "changes": "Add column for sequence_identity_coefficient",
        "reason": "To provide a complete mapping for sequence identity coefficients"
      },
      {
        "table_name": "building",
        "changes": "Add column for building_capacity",
        "reason": "To explicitly map building capacity to a numerical value"
      }
    ]
  },
  "business_configuration_logic_updates": {
    "configuration_parameters": {
      "total_available_resources": {
        "sample_value": "1000",
        "data_type": "INTEGER",
        "business_meaning": "Total resources available for allocation",
        "optimization_role": "Constraint bound for total resources",
        "configuration_type": "scalar_parameter"
      },
      "sequence_identity_coefficient": {
        "sample_value": "0.8",
        "data_type": "FLOAT",
        "business_meaning": "Coefficient representing sequence identity to human proteins",
        "optimization_role": "Objective coefficient",
        "configuration_type": "scalar_parameter"
      },
      "building_capacity": {
        "sample_value": "500",
        "data_type": "INTEGER",
        "business_meaning": "Capacity of the building associated with institution",
        "optimization_role": "Constraint bound for building capacity",
        "configuration_type": "scalar_parameter"
      }
    },
    "updates_rationale": "These parameters are better suited for configuration logic due to their scalar nature and lack of sufficient data for table representation."
  },
  "implemented_optimization_mapping": {
    "objective_coefficients_mapping": {
      "sequence_identity_coefficient[i]": "protein.sequence_identity_coefficient"
    },
    "constraint_bounds_mapping": {
      "total_available_resources": "business_configuration_logic.total_available_resources",
      "building_capacity[i]": "building.building_capacity"
    },
    "decision_variables_mapping": {
      "allocation[i]": "ResourceAllocation.allocation"
    }
  },
  "data_dictionary": {
    "tables": {
      "ResourceAllocation": {
        "business_purpose": "Represents the allocation of resources to each institution",
        "optimization_role": "decision_variables",
        "columns": {
          "institution_id": {
            "data_type": "INTEGER",
            "business_meaning": "Unique identifier for each institution",
            "optimization_purpose": "Links allocation to specific institutions",
            "sample_values": "1, 2, 3"
          },
          "allocation": {
            "data_type": "FLOAT",
            "business_meaning": "Amount of resources allocated to the institution",
            "optimization_purpose": "Decision variable for resource allocation",
            "sample_values": "100.0, 200.0, 300.0"
          }
        }
      },
      "protein": {
        "business_purpose": "Stores protein data including sequence identity coefficients",
        "optimization_role": "objective_coefficients",
        "columns": {
          "sequence_identity_coefficient": {
            "data_type": "FLOAT",
            "business_meaning": "Coefficient representing sequence identity to human proteins",
            "optimization_purpose": "Objective coefficient",
            "sample_values": "0.8, 0.9, 0.85"
          }
        }
      },
      "building": {
        "business_purpose": "Stores building data including capacity",
        "optimization_role": "constraint_bounds",
        "columns": {
          "building_capacity": {
            "data_type": "INTEGER",
            "business_meaning": "Capacity of the building associated with institution",
            "optimization_purpose": "Constraint bound for building capacity",
            "sample_values": "500, 600, 700"
          }
        }
      }
    }
  },
  "data_mapping": {
    "objective_sources": [
      "protein.sequence_identity_coefficient"
    ],
    "constraint_sources": [
      "business_configuration_logic.total_available_resources",
      "building.building_capacity"
    ],
    "sample_data_rows": {
      "ResourceAllocation": 3,
      "protein": 3,
      "building": 3
    }
  },
  "validation": {
    "schema_complete": true,
    "data_consistent": true,
    "math_traceable": true,
    "business_aligned": true,
    "follows_db_standards": true
  }
}
