Dataset Viewer
The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    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 364, in tsfile.tsfile_reader.TsFileReaderPy.__init__
                File "tsfile/tsfile_reader.pyx", line 367, in tsfile.tsfile_reader.TsFileReaderPy.init_reader
                File "tsfile/tsfile_py_cpp.pyx", line 789, in tsfile.tsfile_py_cpp.tsfile_reader_new_c
                File "tsfile/tsfile_py_cpp.pyx", line 55, in tsfile.tsfile_py_cpp.check_error
              tsfile.exceptions.FileOpenError: 28: Failed to open file
              
              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.

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

OIL-041 — Synthetic Spare Parts Demand Dataset (Sample) (TsFile)

Apache TsFile version of xpertsystems/oil041-sample.

Overview

A schema-identical preview of OIL-041, the XpertSystems.ai synthetic spare-parts demand and inventory-optimization dataset for upstream, midstream, and downstream oil & gas operations. The full product covers ~6,000 facilities and ~450,000 parts across a 5-year horizon. This sample is the generator's sample mode (75 facilities × 1,500 parts × 45 warehouses × 365 days) covering all 13 product tables.

  • Tables: 13 TsFile table(s), 99,076 rows in total.
    • emergency_orders: 450 rows, 2021-01-01 05:37:18 → 2021-12-30 01:57:35 (UTC)
    • facility_master: 75 rows, 1970-01-01 00:00:00 → 1970-01-01 00:00:00.074000 (UTC)
    • inventory_levels: 4,372 rows, 1970-01-01 00:00:00 → 1970-01-01 00:00:04.371000 (UTC)
    • maintenance_consumption: 25,000 rows, 2021-01-01 00:14:18 → 2021-12-31 23:04:28 (UTC)
    • procurement_orders: 8,000 rows, 2021-01-01 02:01:03 → 2021-12-31 23:36:41 (UTC)
    • reliability_labels: 4,372 rows, 1970-01-01 00:00:00 → 1970-01-01 00:00:04.371000 (UTC)
    • reorder_recommendations: 4,372 rows, 1970-01-01 00:00:00 → 1970-01-01 00:00:04.371000 (UTC)
    • spare_parts_master: 1,500 rows, 1970-01-01 00:00:00 → 1970-01-01 00:00:01.499000 (UTC)
    • stockout_events: 800 rows, 2021-01-01 07:08:32 → 2021-12-31 23:18:48 (UTC)
    • supplier_lead_times: 15,000 rows, 1970-01-01 00:00:00 → 1970-01-01 00:00:14.999000 (UTC)
    • supplier_master: 90 rows, 1970-01-01 00:00:00 → 1970-01-01 00:00:00.089000 (UTC)
    • warehouse_master: 45 rows, 1970-01-01 00:00:00 → 1970-01-01 00:00:00.044000 (UTC)
    • warehouse_movements: 35,000 rows, 2021-01-01 00:04:09 → 2021-12-31 23:44:07 (UTC)

Schema (TsFile structure)

emergency_orders.tsfile

  • Time (INT64, milliseconds) — the source created_timestamp column.
  • TAG: none — the table is stored as a single device.
  • FIELD (10): emergency_order_id (string), stockout_id (string), facility_id (string), part_id (string), supplier_id (string), emergency_level (string), expedite_flag (int64), order_qty (int64), freight_multiplier (double), approval_override_flag (int64)

facility_master.tsfile

  • Time (INT64, milliseconds) — synthetic row index over the stable source order; this table has no clock column.
  • TAG: none — the table is stored as a single device.
  • FIELD (6): facility_id (string), facility_type (string), region (string), offshore_flag (int64), operational_complexity_score (double), maintenance_intensity_index (double)

inventory_levels.tsfile

  • Time (INT64, milliseconds) — synthetic row index over the stable source order; this table has no clock column.
  • TAG: none — the table is stored as a single device.
  • FIELD (11): inventory_id (string), warehouse_id (string), facility_id (string), part_id (string), stock_qty (int64), reserved_qty (int64), safety_stock_qty (int64), reorder_point_qty (int64), max_stock_qty (int64), inventory_value_usd (double), stockout_risk_score (double)

maintenance_consumption.tsfile

  • Time (INT64, milliseconds) — the source event_timestamp column.
  • TAG: none — the table is stored as a single device.
  • FIELD (10): work_order_id (string), facility_id (string), equipment_type (string), part_id (string), maintenance_type (string), consumption_qty (int64), asset_age_years (double), failure_mode (string), downtime_hours (double), labor_hours (double)

procurement_orders.tsfile

  • Time (INT64, milliseconds) — the source po_date column.
  • TAG: none — the table is stored as a single device.
  • FIELD (11): po_id (string), facility_id (string), supplier_id (string), part_id (string), order_qty (int64), unit_cost_usd (double), order_value_usd (double), approval_status (string), approval_cycle_days (double), emergency_flag (int64), budget_exception_flag (int64)

reliability_labels.tsfile

  • Time (INT64, milliseconds) — synthetic row index over the stable source order; this table has no clock column.
  • TAG: none — the table is stored as a single device.
  • FIELD (10): label_id (string), facility_id (string), warehouse_id (string), part_id (string), stockout_probability (double), failure_probability (double), critical_spare_gap_qty (int64), inventory_health_score (double), recommended_priority (string), ai_training_label (string)

reorder_recommendations.tsfile

  • Time (INT64, milliseconds) — synthetic row index over the stable source order; this table has no clock column.
  • TAG: none — the table is stored as a single device.
  • FIELD (11): recommendation_id (string), facility_id (string), warehouse_id (string), part_id (string), current_stock_qty (int64), reorder_point_qty (int64), recommended_order_qty (int64), estimated_order_value_usd (double), recommendation_policy (string), stockout_risk_score (double), optimization_action (string)

spare_parts_master.tsfile

  • Time (INT64, milliseconds) — synthetic row index over the stable source order; this table has no clock column.
  • TAG: none — the table is stored as a single device.
  • FIELD (13): part_id (string), part_category (string), equipment_type (string), criticality (string), unit_cost_usd (double), standard_lead_time_days (double), mean_monthly_demand (double), failure_linkage_score (double), obsolescence_risk_score (double), repairable_flag (int64), hazmat_flag (int64), serialized_flag (int64), abc_class (string)

stockout_events.tsfile

  • Time (INT64, milliseconds) — the source stockout_start column.
  • TAG: none — the table is stored as a single device.
  • FIELD (10): stockout_id (string), facility_id (string), warehouse_id (string), part_id (string), stockout_duration_hours (double), unplanned_downtime_hours (double), lost_production_usd (double), severity_score (double), root_cause (string), mitigation_action (string)

supplier_lead_times.tsfile

  • Time (INT64, milliseconds) — synthetic row index over the stable source order; this table has no clock column.
  • TAG: none — the table is stored as a single device.
  • FIELD (9): leadtime_id (string), supplier_id (string), part_id (string), quoted_lead_time_days (double), actual_lead_time_days (double), delay_days (double), supplier_reliability_score (double), logistics_disruption_flag (int64), delivery_status (string)

supplier_master.tsfile

  • Time (INT64, milliseconds) — synthetic row index over the stable source order; this table has no clock column.
  • TAG: none — the table is stored as a single device.
  • FIELD (6): supplier_id (string), supplier_tier (string), region (string), supplier_reliability_score (double), contracted_flag (int64), single_source_flag (int64)

warehouse_master.tsfile

  • Time (INT64, milliseconds) — synthetic row index over the stable source order; this table has no clock column.
  • TAG: none — the table is stored as a single device.
  • FIELD (7): warehouse_id (string), facility_id (string), warehouse_type (string), storage_capacity_units (int64), baseline_utilization (double), temperature_controlled_flag (int64), hazmat_storage_flag (int64)

warehouse_movements.tsfile

  • Time (INT64, milliseconds) — the source movement_timestamp column.
  • TAG: none — the table is stored as a single device.
  • FIELD (8): movement_id (string), warehouse_id (string), facility_id (string), part_id (string), movement_type (string), quantity (int64), unit_cost_usd (double), reason_code (string)

Conversion notes

  • One TsFile per source CSV (13 tables); each table is stored as a single device, so the rig / well / tank / equipment / sensor identifiers stay ordinary FIELDs.
  • Time = the table's own clock column as INT64 milliseconds (event timestamps, sample or inspection dates, and daily record dates). The clock columns used here are created_timestamp, event_timestamp, movement_timestamp, po_date, stockout_start.
  • Tables with no clock column (facility_master, inventory_levels, reliability_labels, reorder_recommendations, spare_parts_master, supplier_lead_times, supplier_master, warehouse_master) use a synthetic row index over their stable source order as Time.
  • No column is dropped. Rows that share a timestamp are ordered by a 1 ms step in stable source order, so daily tables keep their exact calendar day.

Usage

Read this dataset with the Java SDK. It is written with the zstd compressor. The Apache TsFile Java SDK reads every compressor, but the Python SDK (pip install tsfile) implements only UNCOMPRESSED, SNAPPY, GZIP and LZ4; opening this file with it yields no rows.

Read a converted file with the Apache TsFile Java SDK (org.apache.tsfile:tsfile):

import org.apache.tsfile.file.metadata.IDeviceID;
import org.apache.tsfile.read.TsFileReader;
import org.apache.tsfile.read.TsFileSequenceReader;
import org.apache.tsfile.read.common.Path;
import org.apache.tsfile.read.expression.QueryExpression;
import org.apache.tsfile.read.query.dataset.QueryDataSet;
import org.apache.tsfile.write.schema.MeasurementSchema;

import java.util.ArrayList;
import java.util.List;

String path = "emergency_orders.tsfile";
try (TsFileSequenceReader sequenceReader = new TsFileSequenceReader(path);
     TsFileReader reader = new TsFileReader(sequenceReader)) {
    // Devices are enumerated as IDeviceID objects. Tables with TAG columns
    // have multi-segment device ids that cannot be rebuilt from their string
    // form, so always use getAllDevices() rather than getAllDeviceIds().
    for (IDeviceID device : sequenceReader.getAllDevices()) {
        List<Path> paths = new ArrayList<>();
        for (MeasurementSchema schema : reader.getMeasurement(device)) {
            String name = schema.getMeasurementName();
            if (name != null && !name.isEmpty() && !name.equalsIgnoreCase("Time")) {
                paths.add(new Path(device, name, true));
            }
        }
        QueryDataSet dataSet = reader.query(QueryExpression.create(paths, null));
        while (dataSet.hasNext()) {
            System.out.println(dataSet.next());
        }
    }
}

Source & license

  • Original dataset: xpertsystems/oil041-sample
  • Source card title: OIL-041 — Synthetic Spare Parts Demand Dataset (Sample)
  • License: cc-by-nc-4.0 (as declared by the original dataset).
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