| --- |
| license: cc-by-4.0 |
| task_categories: |
| - tabular-classification |
| - time-series-forecasting |
| language: |
| - en |
| tags: |
| - tsfile |
| - timeseries |
| - internet-censorship |
| - network-measurement |
| - internet-freedom |
| - ooni |
| - ioda |
| - censoredplanet |
| - time-series |
| - format:tsfile |
| pretty_name: Voidly Benchmark v1 (TsFile) |
| size_categories: |
| - 10K<n<100K |
| configs: |
| - config_name: default |
| data_files: |
| - split: evidence |
| path: evidence.tsfile |
| - split: incident_evidence |
| path: incident_evidence.tsfile |
| - split: incidents |
| path: incidents.tsfile |
| --- |
| |
| # Voidly Benchmark v1 (TsFile) |
|
|
| Apache TsFile version of [`emperor-mew/voidly-bench-v1`](https://huggingface.co/datasets/emperor-mew/voidly-bench-v1). |
|
|
| ## Overview |
|
|
| An evidence-backed internet-measurement benchmark: individual observations from public measurement sources (IODA, OONI, …) with their signal level and confidence, the incident-to-evidence links that group them, and the resulting incident records with severity, mechanism and duration. |
|
|
| - **Tables:** 3 TsFile table(s), 86,546 rows in total. |
| - `evidence`: 49,596 rows, 2016-07-15 00:00:00 → 2026-04-16 18:00:00.093000 (UTC) |
| - `incident_evidence`: 35,376 rows, 1970-01-01 00:00:00 → 2026-04-17 03:47:14.498000 (UTC) |
| - `incidents`: 1,574 rows, 2016-07-15 00:00:00 → 2026-04-12 08:00:00.001000 (UTC) |
|
|
| ## Schema (TsFile structure) |
|
|
| ### `evidence.tsfile` |
|
|
| - **Time** (INT64, milliseconds) — the source `observed_at` column. |
| - **TAG**: none — the table is stored as a single device. |
| - **FIELD** (21): `id` (int64), `source` (string), `kind` (string), `source_ref` (string), `source_url` (string), `retrieved_at` (string), `country_code` (string), `region_code` (string), `asn` (double), `signal_type` (string), `signal_value` (double), `signal_baseline` (double), `signal_level` (string), `upstream_severity` (string), `domain` (string), `continent` (string), `un_subregion` (string), `risk_tier` (double), `domain_category` (string), `blocking_method` (string), `confidence` (double) |
|
|
| ### `incident_evidence.tsfile` |
| |
| - **Time** (INT64, milliseconds) — the source `linked_at` column. |
| - **TAG**: none — the table is stored as a single device. |
| - **FIELD** (4): `incident_id` (string), `evidence_id` (int64), `link_confidence` (double), `link_reason` (string) |
|
|
| ### `incidents.tsfile` |
|
|
| - **Time** (INT64, milliseconds) — the source `first_seen` column. |
| - **TAG**: none — the table is stored as a single device. |
| - **FIELD** (18): `id` (string), `readable_id` (string), `country_code` (string), `title` (string), `incident_type` (string), `mechanism` (string), `severity` (string), `confidence` (double), `status` (string), `last_seen` (string), `duration_hours` (double), `affected_domains` (string), `affected_services` (string), `affected_asns` (string), `measurement_count` (int64), `anomaly_rate` (double), `sources` (string), `revision` (int64) |
|
|
| ## Conversion notes |
|
|
| - One TsFile per source Parquet (3 tables), each stored as a single device. |
| - Time = the observation timestamp of each table as INT64 milliseconds: `observed_at` (internet-measurement evidence), `linked_at` (incident-to-evidence links) and `first_seen` (incidents). `incidents.last_seen` is kept as a FIELD so both ends of an incident survive. |
| - No column is dropped apart from `affected_services` / `affected_asns`, which the source stores with Arrow type `null` and which are empty in every row. |
| - Not converted: `reproduce.py, reproduce_results.json, source_reliability.json, DATA_HASHES.txt, LICENSE` — the reproduction script, its recorded results and integrity hashes are not time series; they are mirrored into the repository as reference files. |
|
|
| ## Usage |
|
|
| Install the Apache TsFile Python SDK (`pip install tsfile`) and read a converted file: |
|
|
| ```python |
| from pathlib import Path |
| from tsfile import TsFileReader |
| |
| path = Path("evidence.tsfile") |
| with TsFileReader(str(path)) as reader: |
| schemas = reader.get_all_table_schemas() |
| print("tables:", list(schemas)) |
| table_name = next(iter(schemas)) |
| table = schemas[table_name] |
| columns = [column.get_column_name() for column in table.get_columns()] |
| print("columns:", columns) |
| field_names = [ |
| column.get_column_name() |
| for column in table.get_columns() |
| if column.get_column_name() not in {"Time", "time"} |
| ] |
| if field_names: |
| with reader.query_table(table_name, field_names[:3], batch_size=1024) as result: |
| batch = result.read_arrow_batch() |
| if batch is not None: |
| print(batch.to_pandas().head()) |
| ``` |
|
|
| ## Source & license |
|
|
| - Original dataset: [`emperor-mew/voidly-bench-v1`](https://huggingface.co/datasets/emperor-mew/voidly-bench-v1) |
| - Source card title / publisher: Voidly Benchmark v1 |
| - License: cc-by-4.0 (as declared by the original dataset). |
|
|
