Iteration final - PROBLEM_DESCRIPTION
Sequence: 7
Timestamp: 2025-07-25 22:32:00

Prompt:
You are a business analyst creating structured optimization problem documentation.

DATA SOURCES EXPLANATION:
- FINAL OR ANALYSIS: Final converged optimization problem from alternating process (iteration 2), contains business context and schema mapping evaluation
- DATABASE SCHEMA: Current database structure after iterative adjustments  
- DATA DICTIONARY: Business meanings and optimization roles of tables and columns
- CURRENT STORED VALUES: Realistic business data generated by triple expert (business + data + optimization)
- BUSINESS CONFIGURATION: Scalar parameters and business logic formulas separated from table data

CRITICAL REQUIREMENTS: 
- Ensure problem description naturally leads to LINEAR or MIXED-INTEGER optimization formulation
- Make business context consistent with the intended decision variables and objectives
- Align constraint descriptions with expected mathematical constraints
- Ensure data descriptions map clearly to expected coefficient sources
- Maintain business authenticity while fixing mathematical consistency issues
- Avoid business scenarios that would naturally require nonlinear relationships (variable products, divisions, etc.)

AUTO-EXTRACTED CONTEXT REQUIREMENTS:
- Business decisions match expected decision variables: x_ij: number of exhibitions artist i participates in in year j (integer)
- Operational parameters align with expected linear objective: maximize ∑(Ticket_Price_j * Attendance_ij * x_ij) where x_ij is the number of exhibitions artist i participates in in year j
- Business configuration includes: Ticket price for exhibition j (used for Objective coefficient for optimization), Expected attendance for artist i in exhibition j (used for Objective coefficient for optimization)
- Use natural language to precisely describe linear mathematical relationships
- NO mathematical formulas, equations, or symbolic notation
- Present data as current operational information
- Focus on precise operational decision-making that leads to linear formulations
- Resource limitations match expected linear constraints
- Avoid scenarios requiring variable products, divisions, or other nonlinear relationships
- Include specific operational parameters that map to expected coefficient sources
- Reference business configuration parameters where appropriate

FINAL OR ANALYSIS:
{
  "database_id": "theme_gallery",
  "iteration": 2,
  "business_context": "A theme gallery aims to maximize its annual revenue from exhibitions by optimizing the allocation of artists to exhibitions based on their popularity and ticket prices, while respecting constraints on artist availability and exhibition capacity.",
  "optimization_problem_description": "The gallery needs to decide how many exhibitions each artist should participate in to maximize total revenue, considering ticket prices and expected attendance, while ensuring no artist is overbooked and each exhibition has a minimum number of artists.",
  "optimization_formulation": {
    "objective": "maximize \u2211(Ticket_Price_j * Attendance_ij * x_ij) where x_ij is the number of exhibitions artist i participates in in year j",
    "decision_variables": "x_ij: number of exhibitions artist i participates in in year j (integer)",
    "constraints": "\u2211(x_ij) \u2264 Max_Exhibitions_i for each artist i, \u2211(x_ij) \u2265 Min_Artists_j for each exhibition j, x_ij \u2265 0 for all i, j"
  },
  "current_optimization_to_schema_mapping": {
    "objective_coefficients": {
      "Ticket_Price_j[j]": {
        "currently_mapped_to": "exhibition_ticket_prices.Ticket_Price",
        "mapping_adequacy": "good",
        "description": "Ticket price for exhibition j"
      },
      "Attendance_ij[i,j]": {
        "currently_mapped_to": "artist_exhibition_attendance.Attendance",
        "mapping_adequacy": "good",
        "description": "Expected attendance for artist i in exhibition j"
      }
    },
    "constraint_bounds": {
      "Max_Exhibitions_i[i]": {
        "currently_mapped_to": "artist_exhibition_limits.Max_Exhibitions",
        "mapping_adequacy": "good",
        "description": "Maximum number of exhibitions artist i can participate in"
      },
      "Min_Artists_j[j]": {
        "currently_mapped_to": "exhibition_artist_requirements.Min_Artists",
        "mapping_adequacy": "good",
        "description": "Minimum number of artists required for exhibition j"
      }
    },
    "decision_variables": {
      "x_ij[i,j]": {
        "currently_mapped_to": "artist_exhibition_assignments.Num_Exhibitions",
        "mapping_adequacy": "good",
        "description": "Number of exhibitions artist i participates in in year j",
        "variable_type": "integer"
      }
    }
  },
  "missing_optimization_requirements": [],
  "iteration_status": {
    "complete": true,
    "confidence": "high",
    "next_focus": "Ready for convergence"
  }
}

FINAL DATABASE SCHEMA:
```sql
-- Iteration 2 Database Schema
-- Objective: Schema changes include creating tables for missing optimization requirements (Ticket_Price_j and Attendance_ij). Business configuration logic updated with scalar parameters and formulas for optimization constraints and calculations.

CREATE TABLE artist_exhibition_limits (
  Artist_ID INTEGER,
  Max_Exhibitions INTEGER
);

CREATE TABLE exhibition_artist_requirements (
  Exhibition_ID INTEGER,
  Min_Artists INTEGER
);

CREATE TABLE artist_exhibition_assignments (
  Artist_ID INTEGER,
  Exhibition_ID INTEGER,
  Num_Exhibitions INTEGER
);

CREATE TABLE exhibition_ticket_prices (
  Exhibition_ID INTEGER,
  Ticket_Price INTEGER
);

CREATE TABLE artist_exhibition_attendance (
  Artist_ID INTEGER,
  Exhibition_ID INTEGER,
  Attendance INTEGER
);


```

CURRENT STORED VALUES:
```sql
-- Iteration 2 Realistic Data
-- Generated by triple expert (business + data + optimization)
-- Values were determined based on realistic business scenarios, considering typical ticket prices, artist popularity, and exhibition capacity. Data was generated to ensure a balanced optimization problem with feasible constraints.

-- Realistic data for artist_exhibition_limits
INSERT INTO artist_exhibition_limits (Artist_ID, Max_Exhibitions) VALUES (1, 5);
INSERT INTO artist_exhibition_limits (Artist_ID, Max_Exhibitions) VALUES (2, 6);
INSERT INTO artist_exhibition_limits (Artist_ID, Max_Exhibitions) VALUES (3, 4);

-- Realistic data for exhibition_artist_requirements
INSERT INTO exhibition_artist_requirements (Exhibition_ID, Min_Artists) VALUES (1, 3);
INSERT INTO exhibition_artist_requirements (Exhibition_ID, Min_Artists) VALUES (2, 4);
INSERT INTO exhibition_artist_requirements (Exhibition_ID, Min_Artists) VALUES (3, 3);

-- Realistic data for artist_exhibition_assignments
INSERT INTO artist_exhibition_assignments (Artist_ID, Exhibition_ID, Num_Exhibitions) VALUES (1, 1, 1);
INSERT INTO artist_exhibition_assignments (Artist_ID, Exhibition_ID, Num_Exhibitions) VALUES (2, 2, 2);
INSERT INTO artist_exhibition_assignments (Artist_ID, Exhibition_ID, Num_Exhibitions) VALUES (3, 3, 1);

-- Realistic data for exhibition_ticket_prices
INSERT INTO exhibition_ticket_prices (Exhibition_ID, Ticket_Price) VALUES (1, 50);
INSERT INTO exhibition_ticket_prices (Exhibition_ID, Ticket_Price) VALUES (2, 70);
INSERT INTO exhibition_ticket_prices (Exhibition_ID, Ticket_Price) VALUES (3, 60);

-- Realistic data for artist_exhibition_attendance
INSERT INTO artist_exhibition_attendance (Artist_ID, Exhibition_ID, Attendance) VALUES (1, 1, 100);
INSERT INTO artist_exhibition_attendance (Artist_ID, Exhibition_ID, Attendance) VALUES (2, 2, 200);
INSERT INTO artist_exhibition_attendance (Artist_ID, Exhibition_ID, Attendance) VALUES (3, 3, 150);


```

DATA DICTIONARY:
{
  "tables": {
    "artist_exhibition_limits": {
      "business_purpose": "Maximum number of exhibitions each artist can participate in",
      "optimization_role": "constraint_bounds",
      "columns": {
        "Artist_ID": {
          "data_type": "INTEGER",
          "business_meaning": "Unique identifier for each artist",
          "optimization_purpose": "Index for artist constraints",
          "sample_values": [
            1,
            2,
            3
          ]
        },
        "Max_Exhibitions": {
          "data_type": "INTEGER",
          "business_meaning": "Maximum number of exhibitions the artist can participate in",
          "optimization_purpose": "Constraint bound for artist participation",
          "sample_values": [
            5,
            6,
            7
          ]
        }
      }
    },
    "exhibition_artist_requirements": {
      "business_purpose": "Minimum number of artists required for each exhibition",
      "optimization_role": "constraint_bounds",
      "columns": {
        "Exhibition_ID": {
          "data_type": "INTEGER",
          "business_meaning": "Unique identifier for each exhibition",
          "optimization_purpose": "Index for exhibition constraints",
          "sample_values": [
            1,
            2,
            3
          ]
        },
        "Min_Artists": {
          "data_type": "INTEGER",
          "business_meaning": "Minimum number of artists required for the exhibition",
          "optimization_purpose": "Constraint bound for exhibition requirements",
          "sample_values": [
            3,
            4,
            5
          ]
        }
      }
    },
    "artist_exhibition_assignments": {
      "business_purpose": "Number of exhibitions each artist participates in per year",
      "optimization_role": "decision_variables",
      "columns": {
        "Artist_ID": {
          "data_type": "INTEGER",
          "business_meaning": "Unique identifier for each artist",
          "optimization_purpose": "Index for artist participation",
          "sample_values": [
            1,
            2,
            3
          ]
        },
        "Exhibition_ID": {
          "data_type": "INTEGER",
          "business_meaning": "Unique identifier for each exhibition",
          "optimization_purpose": "Index for exhibition participation",
          "sample_values": [
            1,
            2,
            3
          ]
        },
        "Num_Exhibitions": {
          "data_type": "INTEGER",
          "business_meaning": "Number of exhibitions the artist participates in",
          "optimization_purpose": "Decision variable for optimization",
          "sample_values": [
            1,
            2,
            3
          ]
        }
      }
    },
    "exhibition_ticket_prices": {
      "business_purpose": "Ticket price for each exhibition",
      "optimization_role": "objective_coefficients",
      "columns": {
        "Exhibition_ID": {
          "data_type": "INTEGER",
          "business_meaning": "Unique identifier for each exhibition",
          "optimization_purpose": "Index for exhibition ticket prices",
          "sample_values": [
            1,
            2,
            3
          ]
        },
        "Ticket_Price": {
          "data_type": "INTEGER",
          "business_meaning": "Ticket price for the exhibition",
          "optimization_purpose": "Objective coefficient for optimization",
          "sample_values": [
            50,
            60,
            70
          ]
        }
      }
    },
    "artist_exhibition_attendance": {
      "business_purpose": "Expected attendance for each artist in each exhibition",
      "optimization_role": "objective_coefficients",
      "columns": {
        "Artist_ID": {
          "data_type": "INTEGER",
          "business_meaning": "Unique identifier for each artist",
          "optimization_purpose": "Index for artist attendance",
          "sample_values": [
            1,
            2,
            3
          ]
        },
        "Exhibition_ID": {
          "data_type": "INTEGER",
          "business_meaning": "Unique identifier for each exhibition",
          "optimization_purpose": "Index for exhibition attendance",
          "sample_values": [
            1,
            2,
            3
          ]
        },
        "Attendance": {
          "data_type": "INTEGER",
          "business_meaning": "Expected attendance for the artist in the exhibition",
          "optimization_purpose": "Objective coefficient for optimization",
          "sample_values": [
            100,
            150,
            200
          ]
        }
      }
    }
  }
}


BUSINESS CONFIGURATION:

BUSINESS CONFIGURATION:
{
  "Ticket_Price_j": {
    "data_type": "INTEGER",
    "business_meaning": "Ticket price for exhibition j",
    "optimization_role": "Objective coefficient for optimization",
    "configuration_type": "scalar_parameter",
    "value": 60,
    "business_justification": "The average ticket price across exhibitions is $60, reflecting a balanced pricing strategy."
  },
  "Attendance_ij": {
    "data_type": "INTEGER",
    "business_meaning": "Expected attendance for artist i in exhibition j",
    "optimization_role": "Objective coefficient for optimization",
    "configuration_type": "scalar_parameter",
    "value": 150,
    "business_justification": "The average expected attendance per artist per exhibition is 150, aligning with typical gallery attendance figures."
  }
}

Business Configuration Design: 
Our system separates business logic design from value determination:
- Configuration Logic (business_configuration_logic.json): Templates designed by data engineers with sample_value for scalars and actual formulas for business logic
- Configuration Values (business_configuration.json): Realistic values determined by domain experts for scalar parameters only
- Design Rationale: Ensures business logic consistency while allowing flexible parameter tuning


TASK: Create structured markdown documentation for SECTIONS 1-3 ONLY (Problem Description).

EXACT MARKDOWN STRUCTURE TO FOLLOW:

# Complete Optimization Problem and Solution: theme_gallery

## 1. Problem Context and Goals

### Context  
[Regenerate business context that naturally aligns with LINEAR optimization formulation. Ensure:]
- Business decisions match expected decision variables: x_ij: number of exhibitions artist i participates in in year j (integer)
- Operational parameters align with expected linear objective: maximize ∑(Ticket_Price_j * Attendance_ij * x_ij) where x_ij is the number of exhibitions artist i participates in in year j
- Business configuration includes: Ticket price for exhibition j (used for Objective coefficient for optimization), Expected attendance for artist i in exhibition j (used for Objective coefficient for optimization)
- Use natural language to precisely describe linear mathematical relationships
- NO mathematical formulas, equations, or symbolic notation
- Present data as current operational information
- Focus on precise operational decision-making that leads to linear formulations
- Resource limitations match expected linear constraints
- Avoid scenarios requiring variable products, divisions, or other nonlinear relationships
- Include specific operational parameters that map to expected coefficient sources
- Reference business configuration parameters where appropriate
- CRITICAL: Include ALL business configuration information (scalar parameters AND business logic formulas) in natural business language

### Goals  
[Regenerate goals that clearly lead to LINEAR mathematical objective:]
- Optimization goal: maximize
- Metric to optimize: maximize ∑(Ticket_Price_j * Attendance_ij * x_ij) where x_ij is the number of exhibitions artist i participates in in year j
- Success measurement aligned with expected coefficient sources
- Use natural language to precisely describe linear optimization goal
- NO mathematical formulas, equations, or symbolic notation

## 2. Constraints    

[Regenerate constraints that directly match expected LINEAR mathematical constraints:]
- Expected constraint: ∑(x_ij) ≤ Max_Exhibitions_i for each artist i, ∑(x_ij) ≥ Min_Artists_j for each exhibition j, x_ij ≥ 0 for all i, j (Form: Standard constraint form based on business requirements)

[Each constraint should be described in business terms that naturally lead to LINEAR mathematical forms (no variable products or divisions)]

## 3. Available Data  

### Database Schema  
```sql
-- Iteration 2 Database Schema
-- Objective: Schema changes include creating tables for missing optimization requirements (Ticket_Price_j and Attendance_ij). Business configuration logic updated with scalar parameters and formulas for optimization constraints and calculations.

CREATE TABLE artist_exhibition_limits (
  Artist_ID INTEGER,
  Max_Exhibitions INTEGER
);

CREATE TABLE exhibition_artist_requirements (
  Exhibition_ID INTEGER,
  Min_Artists INTEGER
);

CREATE TABLE artist_exhibition_assignments (
  Artist_ID INTEGER,
  Exhibition_ID INTEGER,
  Num_Exhibitions INTEGER
);

CREATE TABLE exhibition_ticket_prices (
  Exhibition_ID INTEGER,
  Ticket_Price INTEGER
);

CREATE TABLE artist_exhibition_attendance (
  Artist_ID INTEGER,
  Exhibition_ID INTEGER,
  Attendance INTEGER
);


```

### Data Dictionary  
[Create comprehensive business-oriented data dictionary mapping tables and columns to their business purposes and optimization roles - NOT technical database terms. Base this on the data_dictionary provided but rewrite in business language that clearly connects to the expected linear mathematical formulation]

### Current Stored Values  
```sql
-- Iteration 2 Realistic Data
-- Generated by triple expert (business + data + optimization)
-- Values were determined based on realistic business scenarios, considering typical ticket prices, artist popularity, and exhibition capacity. Data was generated to ensure a balanced optimization problem with feasible constraints.

-- Realistic data for artist_exhibition_limits
INSERT INTO artist_exhibition_limits (Artist_ID, Max_Exhibitions) VALUES (1, 5);
INSERT INTO artist_exhibition_limits (Artist_ID, Max_Exhibitions) VALUES (2, 6);
INSERT INTO artist_exhibition_limits (Artist_ID, Max_Exhibitions) VALUES (3, 4);

-- Realistic data for exhibition_artist_requirements
INSERT INTO exhibition_artist_requirements (Exhibition_ID, Min_Artists) VALUES (1, 3);
INSERT INTO exhibition_artist_requirements (Exhibition_ID, Min_Artists) VALUES (2, 4);
INSERT INTO exhibition_artist_requirements (Exhibition_ID, Min_Artists) VALUES (3, 3);

-- Realistic data for artist_exhibition_assignments
INSERT INTO artist_exhibition_assignments (Artist_ID, Exhibition_ID, Num_Exhibitions) VALUES (1, 1, 1);
INSERT INTO artist_exhibition_assignments (Artist_ID, Exhibition_ID, Num_Exhibitions) VALUES (2, 2, 2);
INSERT INTO artist_exhibition_assignments (Artist_ID, Exhibition_ID, Num_Exhibitions) VALUES (3, 3, 1);

-- Realistic data for exhibition_ticket_prices
INSERT INTO exhibition_ticket_prices (Exhibition_ID, Ticket_Price) VALUES (1, 50);
INSERT INTO exhibition_ticket_prices (Exhibition_ID, Ticket_Price) VALUES (2, 70);
INSERT INTO exhibition_ticket_prices (Exhibition_ID, Ticket_Price) VALUES (3, 60);

-- Realistic data for artist_exhibition_attendance
INSERT INTO artist_exhibition_attendance (Artist_ID, Exhibition_ID, Attendance) VALUES (1, 1, 100);
INSERT INTO artist_exhibition_attendance (Artist_ID, Exhibition_ID, Attendance) VALUES (2, 2, 200);
INSERT INTO artist_exhibition_attendance (Artist_ID, Exhibition_ID, Attendance) VALUES (3, 3, 150);


```

STOP HERE - Do not include section 4 (Mathematical Optimization Formulation).

Response:
# Complete Optimization Problem and Solution: theme_gallery

## 1. Problem Context and Goals

### Context  
The theme gallery operates with the goal of maximizing its annual revenue from exhibitions. To achieve this, the gallery must strategically allocate artists to exhibitions based on their popularity and the ticket prices of the exhibitions. Each artist has a limited availability, and each exhibition requires a minimum number of artists to ensure its success. The gallery’s decision-making process involves determining the number of exhibitions each artist should participate in during the year. This decision is influenced by two key factors: the ticket price of each exhibition and the expected attendance for each artist in each exhibition. The ticket price for each exhibition is a fixed value, and the expected attendance is based on historical data and artist popularity. The gallery must ensure that no artist is overbooked and that each exhibition meets its minimum artist requirement. These operational parameters are designed to align with a linear optimization framework, ensuring that the relationships between variables are straightforward and avoid any nonlinear complexities such as variable products or divisions.

### Goals  
The primary goal of the optimization is to maximize the gallery’s total annual revenue from exhibitions. This revenue is calculated by multiplying the ticket price of each exhibition by the expected attendance for each artist in that exhibition and then summing these values across all artists and exhibitions. Success is measured by the total revenue generated, which directly depends on the strategic allocation of artists to exhibitions. The optimization process ensures that this allocation respects the constraints of artist availability and exhibition requirements, leading to a feasible and profitable solution.

## 2. Constraints  

The optimization problem is subject to the following constraints:  
1. **Artist Availability Constraint**: Each artist can participate in a limited number of exhibitions per year. The total number of exhibitions an artist is assigned to must not exceed their maximum allowed number of exhibitions.  
2. **Exhibition Requirement Constraint**: Each exhibition must have a minimum number of artists participating to ensure its success. The total number of artists assigned to an exhibition must meet or exceed this minimum requirement.  
3. **Non-Negativity Constraint**: The number of exhibitions an artist participates in must be a non-negative integer, ensuring that the solution is realistic and implementable.  

These constraints are designed to reflect real-world operational limitations and are expressed in a way that naturally leads to a linear mathematical formulation.

## 3. Available Data  

### Database Schema  
```sql
-- Iteration 2 Database Schema
-- Objective: Schema changes include creating tables for missing optimization requirements (Ticket_Price_j and Attendance_ij). Business configuration logic updated with scalar parameters and formulas for optimization constraints and calculations.

CREATE TABLE artist_exhibition_limits (
  Artist_ID INTEGER,
  Max_Exhibitions INTEGER
);

CREATE TABLE exhibition_artist_requirements (
  Exhibition_ID INTEGER,
  Min_Artists INTEGER
);

CREATE TABLE artist_exhibition_assignments (
  Artist_ID INTEGER,
  Exhibition_ID INTEGER,
  Num_Exhibitions INTEGER
);

CREATE TABLE exhibition_ticket_prices (
  Exhibition_ID INTEGER,
  Ticket_Price INTEGER
);

CREATE TABLE artist_exhibition_attendance (
  Artist_ID INTEGER,
  Exhibition_ID INTEGER,
  Attendance INTEGER
);
```

### Data Dictionary  
- **artist_exhibition_limits**: Tracks the maximum number of exhibitions each artist can participate in.  
  - *Artist_ID*: Unique identifier for each artist.  
  - *Max_Exhibitions*: Maximum number of exhibitions the artist can participate in.  

- **exhibition_artist_requirements**: Specifies the minimum number of artists required for each exhibition.  
  - *Exhibition_ID*: Unique identifier for each exhibition.  
  - *Min_Artists*: Minimum number of artists required for the exhibition.  

- **artist_exhibition_assignments**: Records the number of exhibitions each artist participates in per year.  
  - *Artist_ID*: Unique identifier for each artist.  
  - *Exhibition_ID*: Unique identifier for each exhibition.  
  - *Num_Exhibitions*: Number of exhibitions the artist participates in.  

- **exhibition_ticket_prices**: Stores the ticket price for each exhibition.  
  - *Exhibition_ID*: Unique identifier for each exhibition.  
  - *Ticket_Price*: Ticket price for the exhibition.  

- **artist_exhibition_attendance**: Captures the expected attendance for each artist in each exhibition.  
  - *Artist_ID*: Unique identifier for each artist.  
  - *Exhibition_ID*: Unique identifier for each exhibition.  
  - *Attendance*: Expected attendance for the artist in the exhibition.  

### Current Stored Values  
```sql
-- Iteration 2 Realistic Data
-- Generated by triple expert (business + data + optimization)
-- Values were determined based on realistic business scenarios, considering typical ticket prices, artist popularity, and exhibition capacity. Data was generated to ensure a balanced optimization problem with feasible constraints.

-- Realistic data for artist_exhibition_limits
INSERT INTO artist_exhibition_limits (Artist_ID, Max_Exhibitions) VALUES (1, 5);
INSERT INTO artist_exhibition_limits (Artist_ID, Max_Exhibitions) VALUES (2, 6);
INSERT INTO artist_exhibition_limits (Artist_ID, Max_Exhibitions) VALUES (3, 4);

-- Realistic data for exhibition_artist_requirements
INSERT INTO exhibition_artist_requirements (Exhibition_ID, Min_Artists) VALUES (1, 3);
INSERT INTO exhibition_artist_requirements (Exhibition_ID, Min_Artists) VALUES (2, 4);
INSERT INTO exhibition_artist_requirements (Exhibition_ID, Min_Artists) VALUES (3, 3);

-- Realistic data for artist_exhibition_assignments
INSERT INTO artist_exhibition_assignments (Artist_ID, Exhibition_ID, Num_Exhibitions) VALUES (1, 1, 1);
INSERT INTO artist_exhibition_assignments (Artist_ID, Exhibition_ID, Num_Exhibitions) VALUES (2, 2, 2);
INSERT INTO artist_exhibition_assignments (Artist_ID, Exhibition_ID, Num_Exhibitions) VALUES (3, 3, 1);

-- Realistic data for exhibition_ticket_prices
INSERT INTO exhibition_ticket_prices (Exhibition_ID, Ticket_Price) VALUES (1, 50);
INSERT INTO exhibition_ticket_prices (Exhibition_ID, Ticket_Price) VALUES (2, 70);
INSERT INTO exhibition_ticket_prices (Exhibition_ID, Ticket_Price) VALUES (3, 60);

-- Realistic data for artist_exhibition_attendance
INSERT INTO artist_exhibition_attendance (Artist_ID, Exhibition_ID, Attendance) VALUES (1, 1, 100);
INSERT INTO artist_exhibition_attendance (Artist_ID, Exhibition_ID, Attendance) VALUES (2, 2, 200);
INSERT INTO artist_exhibition_attendance (Artist_ID, Exhibition_ID, Attendance) VALUES (3, 3, 150);
```
