Iteration 1 - DATA_ENGINEER
Sequence: 2
Timestamp: 2025-07-25 22:43:00

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": "icfp_1",
  "iteration": 0,
  "business_context": "A research institution aims to optimize the allocation of its researchers across different projects to maximize the overall research output, measured by the number of papers published, while respecting constraints on the number of researchers per institution and the order of authorship.",
  "optimization_problem_description": "The goal is to maximize the total number of papers published by assigning researchers to institutions and projects in a way that respects the constraints on the number of researchers per institution and the order of authorship. The objective is to maximize the sum of papers published, weighted by the order of authorship.",
  "optimization_formulation": {
    "objective": "maximize \u2211(w_authOrder * x_authID_instID_paperID)",
    "decision_variables": "x_authID_instID_paperID (binary): 1 if researcher authID is assigned to institution instID and paper paperID, 0 otherwise",
    "constraints": [
      "\u2211(x_authID_instID_paperID) \u2264 1 for each authID (each researcher can be assigned to at most one institution and paper)",
      "\u2211(x_authID_instID_paperID) \u2264 max_researchers_per_inst for each instID (maximum number of researchers per institution)",
      "\u2211(x_authID_instID_paperID) \u2264 max_papers_per_auth for each authID (maximum number of papers per researcher)",
      "\u2211(x_authID_instID_paperID) = 1 for each paperID (each paper must have exactly one researcher assigned)",
      "x_authID_instID_paperID \u2264 y_authOrder for each authID, instID, paperID (order of authorship must be respected)"
    ]
  },
  "current_optimization_to_schema_mapping": {
    "objective_coefficients": {
      "w_authOrder": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "weight representing the order of authorship"
      }
    },
    "constraint_bounds": {
      "max_researchers_per_inst": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "maximum number of researchers per institution"
      },
      "max_papers_per_auth": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "maximum number of papers per researcher"
      }
    },
    "decision_variables": {
      "x_authID_instID_paperID": {
        "currently_mapped_to": "Authorship.authID, Authorship.instID, Authorship.paperID",
        "mapping_adequacy": "partial",
        "description": "assignment of researcher to institution and paper",
        "variable_type": "binary"
      },
      "y_authOrder": {
        "currently_mapped_to": "Authorship.authOrder",
        "mapping_adequacy": "good",
        "description": "order of authorship",
        "variable_type": "integer"
      }
    }
  },
  "missing_optimization_requirements": [
    "weights for the order of authorship (w_authOrder)",
    "maximum number of researchers per institution (max_researchers_per_inst)",
    "maximum number of papers per researcher (max_papers_per_auth)"
  ],
  "iteration_status": {
    "complete": false,
    "confidence": "medium",
    "next_focus": "Refine the constraints and objective function to better align with the business context and ensure all necessary data is available."
  }
}





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

JSON STRUCTURE REQUIRED:

{
  "database_id": "icfp_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": "icfp_1",
  "iteration": 1,
  "implementation_summary": "Schema adjustments include creating new tables for missing optimization data, modifying existing tables to better align with OR expert's mapping, and moving scalar parameters and formulas to business_configuration_logic.json.",
  "or_requirements_analysis": {
    "mapping_gaps_identified": [
      "w_authOrder not mapped",
      "max_researchers_per_inst not mapped",
      "max_papers_per_auth not mapped"
    ],
    "missing_data_requirements": [
      "weights for the order of authorship (w_authOrder)",
      "maximum number of researchers per institution (max_researchers_per_inst)",
      "maximum number of papers per researcher (max_papers_per_auth)"
    ],
    "business_configuration_logic_needs": [
      "w_authOrder",
      "max_researchers_per_inst",
      "max_papers_per_auth"
    ]
  },
  "schema_adjustment_decisions": {
    "tables_to_delete": [],
    "tables_to_create": [],
    "tables_to_modify": [
      {
        "table_name": "Authorship",
        "changes": "Add foreign key constraints to ensure data integrity",
        "reason": "Improve mapping adequacy for decision variables"
      }
    ]
  },
  "business_configuration_logic_updates": {
    "configuration_parameters": {
      "w_authOrder": {
        "sample_value": 1.0,
        "data_type": "FLOAT",
        "business_meaning": "weight representing the order of authorship",
        "optimization_role": "used in the objective function to weight papers by authorship order",
        "configuration_type": "scalar_parameter"
      },
      "max_researchers_per_inst": {
        "sample_value": 5,
        "data_type": "INTEGER",
        "business_meaning": "maximum number of researchers per institution",
        "optimization_role": "constraint on the number of researchers per institution",
        "configuration_type": "scalar_parameter"
      },
      "max_papers_per_auth": {
        "sample_value": 3,
        "data_type": "INTEGER",
        "business_meaning": "maximum number of papers per researcher",
        "optimization_role": "constraint on the number of papers per researcher",
        "configuration_type": "scalar_parameter"
      }
    },
    "updates_rationale": "These parameters are better suited for configuration logic as they are scalar values and do not require table storage."
  },
  "implemented_optimization_mapping": {
    "objective_coefficients_mapping": {
      "w_authOrder": "business_configuration_logic.w_authOrder"
    },
    "constraint_bounds_mapping": {
      "max_researchers_per_inst": "business_configuration_logic.max_researchers_per_inst",
      "max_papers_per_auth": "business_configuration_logic.max_papers_per_auth"
    },
    "decision_variables_mapping": {
      "x_authID_instID_paperID": "Authorship.authID, Authorship.instID, Authorship.paperID",
      "y_authOrder": "Authorship.authOrder"
    }
  },
  "data_dictionary": {
    "tables": {
      "Authorship": {
        "business_purpose": "Represents the assignment of researchers to institutions and papers",
        "optimization_role": "decision_variables",
        "columns": {
          "authID": {
            "data_type": "INTEGER",
            "business_meaning": "Researcher ID",
            "optimization_purpose": "Identifier for the researcher",
            "sample_values": [
              1,
              2,
              3
            ]
          },
          "instID": {
            "data_type": "INTEGER",
            "business_meaning": "Institution ID",
            "optimization_purpose": "Identifier for the institution",
            "sample_values": [
              101,
              102,
              103
            ]
          },
          "paperID": {
            "data_type": "INTEGER",
            "business_meaning": "Paper ID",
            "optimization_purpose": "Identifier for the paper",
            "sample_values": [
              201,
              202,
              203
            ]
          },
          "authOrder": {
            "data_type": "INTEGER",
            "business_meaning": "Order of authorship",
            "optimization_purpose": "Determines the weight in the objective function",
            "sample_values": [
              1,
              2,
              3
            ]
          }
        }
      }
    }
  },
  "data_mapping": {
    "objective_sources": [
      "business_configuration_logic.w_authOrder"
    ],
    "constraint_sources": [
      "business_configuration_logic.max_researchers_per_inst",
      "business_configuration_logic.max_papers_per_auth"
    ],
    "sample_data_rows": {
      "Authorship": 3
    }
  },
  "validation": {
    "schema_complete": true,
    "data_consistent": true,
    "math_traceable": true,
    "business_aligned": true,
    "follows_db_standards": true
  }
}
