Datasets:

THULab
/

africa-synth-telecom-internet-of-things-machine-to-nigeria

Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "tsfile/tsfile_py_cpp.pyx", line 567, in tsfile.tsfile_py_cpp.tsfile_reader_new_c
              tsfile.exceptions.FileOpenError: 28: 
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/tsfile/tsfile.py", line 271, in _split_generators
                  scan = self._scan_metadata(all_files)
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/tsfile/tsfile.py", line 318, in _scan_metadata
                  with self._open_reader(file) as reader:
                       ~~~~~~~~~~~~~~~~~^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/tsfile/tsfile.py", line 742, in _open_reader
                  return TsFileReader(file)
                File "tsfile/tsfile_reader.pyx", line 323, in tsfile.tsfile_reader.TsFileReaderPy.__init__
              SystemError: <class '_weakrefset.WeakSet'> returned a result with an exception set
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 68, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ~~~~~~~~~~~~~~~~~~~~~~~^
                      path=dataset,
                      ^^^^^^^^^^^^^
                      config_name=config,
                      ^^^^^^^^^^^^^^^^^^^
                      token=hf_token,
                      ^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                      path,
                  ...<6 lines>...
                      **config_kwargs,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

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Internet of Things Machine to Machine Traffic (TsFile)

This dataset is an Apache TsFile conversion of electricsheepafrica/africa-synth-telecom-internet-of-things-machine-to-nigeria, a synthetic Nigerian telecom Internet-of-Things / Machine-to-Machine traffic log dataset.

Source Dataset

Original dataset: electricsheepafrica/africa-synth-telecom-internet-of-things-machine-to-nigeria
Dataset title: Internet of Things Machine to Machine Traffic
Category: Network Performance and Operations
Source format: CSV and Parquet
Source scale: 300,000 rows x 12 columns, no train/test split
Date generated by source: 2025-10-05
Modalities: Time-series
Language: English
Source tags: telecom, iot, m2m
License note: the Hugging Face dataset card metadata declares gpl; the source README body says "MIT License - For educational and research purposes".

Converted Data

TsFile: africa_telecom_iot_m2m_traffic_nigeria.tsfile
Table name: internet_of_things_machine_to_machine_traffic
Rows converted: 300,000
Staged table shape: 300,000 rows x 13 columns
Time range: 2025-09-01 00:00:02 to 2025-10-01 23:59:56
Time precision: milliseconds (Time is epoch-ms INT64)
No source rows are intentionally dropped.

Schema Mapping

Column	Role	Notes
`Time`	TIME	Converted from source `timestamp` to epoch milliseconds.
`device_type`	TAG	Low-cardinality device category.
`operator`	TAG	Nigerian telecom operator.
`city`	TAG	City associated with the traffic log.
`network_type`	TAG	3G / 4G / 5G network type values in this source file.
`event_rank`	TAG	Generated rank for concurrent records with the same categorical TAG values and `Time`.
`transaction_id`	FIELD	Source string identifier.
`device_id`	FIELD	Source string identifier; high cardinality, therefore not used as a TAG.
`tower_id`	FIELD	Source string identifier; high cardinality, therefore not used as a TAG.
`data_size_mb`	FIELD	Source numeric traffic size in MB.
`connection_duration_seconds`	FIELD	Source integer connection duration in seconds.
`transmission_status`	FIELD	Source string status.
`signal_quality`	FIELD	Source string signal quality.

Conversion Notes

The source timestamp column is replaced by the TsFile Time column and is not retained as a duplicate FIELD.
This is an event/log table. High-cardinality identifiers (transaction_id, device_id, tower_id) are stored as FIELD columns rather than device keys.
The TAG columns are low-cardinality categorical dimensions plus the generated event_rank column.
event_rank preserves all records where multiple events share the same (device_type, operator, city, network_type, Time) key without shifting source timestamps.
Local validation confirmed 300,000 staged Parquet rows and 300,000 TsFile metadata rows.

Minimal Read Example

from tsfile import TsFileReader
from tsfile import tag_eq

path = "africa_telecom_iot_m2m_traffic_nigeria.tsfile"
reader = TsFileReader(path)

# Inspect table schemas.
print(reader.get_all_table_schemas().keys())

# Query FIELD columns; optionally add tag_filter=tag_eq("operator", "MTN").
with reader.query_table(
    "internet_of_things_machine_to_machine_traffic",
    ["transaction_id", "device_id", "data_size_mb", "connection_duration_seconds"],
    batch_size=1024,
) as rs:
    batch = rs.read_arrow_batch()
    print(batch)

reader.close()

Downloads last month: 28

Total file size:

11.6 MB