Create a dataset

# Data Model Experiment
 
Create a dataset with the following three-table structure:
 
```sql
CREATE TABLE authors (
  author_id INT PRIMARY KEY,
  name VARCHAR(100)
);
 
CREATE TABLE books (
  book_id INT PRIMARY KEY,
  title VARCHAR(100),
  author_id INT,
  FOREIGN KEY (author_id) REFERENCES authors(author_id)
);
 
CREATE TABLE reviews (
  review_id INT PRIMARY KEY,
  book_id INT,
  rating INT,
  FOREIGN KEY (book_id) REFERENCES books(book_id)
);
```

# Dataset Documentation: `estat_hlth_hc_phys2.xml`
 
## 📄 Dataset Description
 
This dataset, sourced from Eurostat, provides annual data on the average number of physician consultations per inhabitant across European countries. It includes two healthcare consultation types: **total consultations (CONSULT)** and **physician consultations only (PCONSULT)**. The data is structured according to the SDMX-ML 2.1 format, ideal for time series analysis and cross-country healthcare comparison.
 
---
 
## 📊 Dataset Size and Shape
 
- **Countries (geo):** 35+ (e.g., AT, BE, DE, FR, etc.)
- **Time span:** Varies by country, generally between 1960 and 2023
- **Observations:** ~2,000+
- **Structure:** Time series, one observation per year per country per healthcare type
 
---
 
## 🧾 Field Definitions and Data Types
 
| Field         | Description                                    | Type    | Notes                                                   |
| ------------- | ---------------------------------------------- | ------- | ------------------------------------------------------- |
| `geo`         | Country code (ISO 2-letter)                    | String  | Example: "AT" = Austria                                 |
| `unit`        | Unit of measure                                | String  | Always `NR_HAB` (number per inhabitant)                 |
| `freq`        | Frequency of observation                       | String  | Always `A` (Annual)                                     |
| `hlthcare`    | Healthcare measure type                        | String  | Either `CONSULT` or `PCONSULT`                          |
| `TIME_PERIOD` | Observation year                               | Integer | E.g., 2020                                              |
| `OBS_VALUE`   | Average number of consultations per inhabitant | Float   | E.g., 9.72                                              |
| `OBS_FLAG`    | Observation status (optional)                  | String  | Flags like 'd', 'b', 'p' (provisional, estimated, etc.) |
 
---
 
## 📈 Helpful Analyses to Perform
 
- **Descriptive Statistics:**
  - Mean, median, min/max consultation rate per year and country.
- **Time Series Analysis:**
  - Trends over time within countries or groups (e.g., EU-27, Nordic countries).
- **Impact of COVID-19:**
  - Year-on-year changes between 2019–2021.
- **Clustering:**
  - Group countries by similar healthcare utilization patterns.
- **Correlation Analysis:**
  - Between consultation rates and other public health indicators (if merged with other datasets).
- **Flag Interpretation:**
  - Understand and possibly filter or highlight flagged data entries (`OBS_FLAG`).
 
---
 
## 📂 Source
 
Extracted from: `estat_hlth_hc_phys2.xml`, structured using SDMX (Statistical Data and Metadata eXchange).
 
Date of extraction: July 15, 2025
Publisher: Eurostat
 
---

# Initial behavior
 
When connecting to this remote data source via connector, the assistant should:
 
1. **Confirm the connection** and print a success or failure message.
2. **Print out all available locations**, schema names, or logical partitions present in the dataset (e.g., country names, data folders, table groups, etc.).
3. **Print out the shape and size** of the dataset(s), including:
 
   - Number of rows and columns
   - Estimated memory footprint
   - Data types summary (categorical, numerical, datetime, etc.)
 
4. **Suggest first exploratory actions**, such as:
 
   - List all column names and infer their types or usage
   - Show missing values per column
   - Plot a histogram of numerical fields
   - Summarize text columns (e.g., most common values)
   - Identify potential primary keys or date fields
   - Generate a correlation matrix for numeric features
   - Detect outliers or anomalies
   - Suggest useful filters or groupings
 
5. **Ask how to proceed**, with suggestions like:
 
   - "Would you like to filter by a specific location?"
   - "Should I analyze a specific column or feature?"
   - "Would you like to view statistics grouped by time period?"
   - "Do you want me to automatically detect relationships between columns?"
 
6. **Set up an interactive flow**, where the user can choose to:
   - Drill down by location, feature, or date range
   - Export part of the dataset
   - Visualize specific fields
   - Apply transformations (e.g., pivot, normalize, deduplicate)