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| 1 |
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---
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| 2 |
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license: cc-by-4.0
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language:
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- en
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| 5 |
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- fr
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tags:
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- smart-building
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- benchmark
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- timeseries
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- graph-database
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- postgresql
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- memgraph
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- sparql
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- building-automation
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task_categories:
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- time-series-forecasting
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- graph-ml
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size_categories:
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- 100M<n<1B
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configs:
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- config_name: default
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data_files:
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- split: graph_nodes
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path: data/graph/nodes.parquet
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- split: graph_edges
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path: data/graph/edges.parquet
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- split: timeseries
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path: data/timeseries/*.parquet
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---
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# BaseType Benchmark Dataset
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Reference dataset for benchmarking database paradigms on smart building data.
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## Paper
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TBD
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## Quick Start
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```python
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from basetype_benchmark.dataset.huggingface import load_benchmark_data
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# Load a specific profile
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data = load_benchmark_data(scale="medium", duration="1m")
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print(f"Nodes: {len(data['nodes'])}")
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print(f"Edges: {len(data['edges'])}")
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print(f"Timeseries points: {len(data['timeseries'])}")
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```
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## Available Profiles
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### Scale (Taille du graphe)
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| Scale | Description | Points | Buildings |
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|-------|-------------|--------|-----------|
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| `small` | Single building | ~50k | 1 |
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| 59 |
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| `medium` | Small campus | ~100k | 5 |
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| `large` | Full campus | ~500k | 10+ |
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### Duration (Priode temporelle)
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| Duration | Description | Days |
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|----------|-------------|------|
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| `1d` | One day | 1 |
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| `1w` | One week | 7 |
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| `1m` | One month | 30 |
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| `6m` | Six months | 180 |
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| `1y` | One year | 365 |
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## Dataset Structure
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```
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data/
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graph/
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nodes.parquet # ~50k nodes across 9 domains
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edges.parquet # ~200k relationships
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timeseries/
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2024-01.parquet # Partitioned by month
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2024-02.parquet
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...
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```
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### Domains Covered
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1. **Spatial**: Site, Building, Floor, Space, Zone
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2. **Equipment**: Systems, Equipment, Sensors, Actuators
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3. **Energy**: Meters (tree distribution), EnergyZones
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4. **IT/Datacenter**: Servers, Racks, Network devices
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5. **Audiovisual**: AV Systems, Displays, Projectors
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6. **Parking**: Zones, Spots, Charging stations
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7. **Security**: Access points, Cameras, Alarms
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8. **Organization**: Departments, Teams, Persons
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9. **Contractual**: Contracts, Providers, Work orders
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## Schema
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### nodes.parquet
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| Column | Type | Description |
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|--------|------|-------------|
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| `node_id` | string | Unique identifier |
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| `node_type` | string | Type (Building, Equipment, etc.) |
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| `name` | string | Human-readable name |
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| `building_id` | int | Building ID for scale filtering (0=cross-building) |
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| `properties` | json | Domain-specific attributes |
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### edges.parquet
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| Column | Type | Description |
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|--------|------|-------------|
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| `src` | string | Source node ID |
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| `dst` | string | Target node ID |
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| `rel` | string | Relationship type |
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### timeseries/*.parquet
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| Column | Type | Description |
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|--------|------|-------------|
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| `point_id` | string | Reference to Point node |
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| `timestamp` | timestamp | Measurement time |
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| `value` | float64 | Measured value |
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| `building_id` | int | Building ID for scale filtering |
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| `year_month` | string | Partition key (YYYY-MM) |
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## Scale Filtering
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| 128 |
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The dataset uses `building_id` for scale-based extraction:
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- **small**: `building_id = 1` (single building)
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- **medium**: `building_id IN (1,2,3,4,5)` (5 buildings)
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- **large**: all buildings (no filter)
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- `building_id = 0`: cross-building entities (Site, Organization, etc.)
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## Benchmark Usage
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| 136 |
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```bash
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| 138 |
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# Run benchmark with specific profile
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| 139 |
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python -m basetype_benchmark.run --scale=medium --duration=1m
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# Results include:
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# - Query latencies (p50, p95, min, max)
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| 143 |
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# - Memory consumption (steady state, peak)
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| 144 |
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# - CPU usage (average, spikes)
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# - Energy estimation (via RAPL/CPU-time)
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```
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## Author
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| 149 |
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Antoine Debienne
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| 151 |
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| 152 |
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## Citation
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| 153 |
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| 154 |
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```bibtex
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| 155 |
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@dataset{basetype_benchmark_2025,
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| 156 |
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author = {Antoine Debienne},
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title = {BaseType Benchmark Dataset},
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| 158 |
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year = {2025},
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publisher = {Hugging Face},
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| 160 |
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url = {https://huggingface.co/datasets/synaptikAD/basetype-benchmark}
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}
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| 162 |
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```
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## License
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| 165 |
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| 166 |
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CC-BY-4.0
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