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--- |
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pretty_name: BGL |
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dataset_name: logfit-project/BGL |
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task_categories: |
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- text-classification |
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language: |
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- en |
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size_categories: |
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- 1M<n<10M |
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annotations_creators: |
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- logfit-project |
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license: other |
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--- |
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# Dataset Card for logfit-project/BGL |
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## Dataset Summary |
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The BlueGene/L (BGL) dataset contains console logs emitted by a 131,072-processor BlueGene/L supercomputer |
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operated at Lawrence Livermore National Laboratory. Each line records hardware or software events tagged |
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with an alert category, enabling downstream research on alert detection, prediction, and log analytics. |
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## Supported Tasks and Leaderboards |
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- anomaly-detection: binary or multi-class classification of alert categories. |
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- log-parsing: template discovery and sequence modeling for high-performance computing (HPC) systems. |
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## Dataset Structure |
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- `label`: Alert category tag (`-` indicates a non-alert informational message). |
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- `timestamp`: Unix epoch timestamp associated with the log event. |
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- `date`: Calendar date formatted as `YYYY.MM.DD`. |
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- `node`: Hardware node identifier that emitted the log line. |
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- `time`: Precise timestamp including microseconds (`YYYY-MM-DD-HH.MM.SS.xxxxxx`). |
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- `node_repeat`: Repeated node identifier found in the structured dataset. |
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- `type`: High-level event type (e.g., `RAS`). |
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- `component`: Subsystem reporting the log (e.g., `KERNEL`). |
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- `level`: Severity level accompanying the event. |
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- `content`: Verbose description of the underlying event. |
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- `anomaly`: Binary indicator (`1` for alert labels, `0` for non-alert `-`). |
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## Source Data |
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- **Homepage:** https://github.com/logpai/loghub/tree/master/BGL |
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- **Original Maintainers:** The LogPAI team (https://logpai.com/). |
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- **Additional Context:** https://www.usenix.org/cfdr-data#hpc4 |
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## Dataset Creation |
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The raw BlueGene/L logs are parsed with a deterministic regular expression to reproduce the schema |
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published in `BGL_2k.log_structured.csv`. The transformation streams lines sequentially so the full corpus |
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can be processed without loading the entire file into memory. |
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## Uses |
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Suitable for supervised alert detection, failure prediction, anomaly detection, sequence modeling of HPC |
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logs, and benchmarking log parsing techniques. |
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## Citation |
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- Adam J. Oliner, Jon Stearley. "What Supercomputers Say: A Study of Five System Logs", DSN 2007. |
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- Jieming Zhu, Shilin He, Pinjia He, Jinyang Liu, Michael R. Lyu. "Loghub: A Large Collection of System Log |
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Datasets for AI-driven Log Analytics", ISSRE 2023. |
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## Dataset Statistics |
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- Number of log lines: 4747963 |
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