README.md

metadata

title: harpertokenSysMon Dataset
tags:
  - system-monitoring
  - time-series
  - anomaly-detection
  - predictive-maintenance
  - macOS
license: mit
language:
  - en

harpertokenSysMon Dataset

Dataset Summary

This open-source dataset captures real-time system metrics from macOS for time-series analysis, anomaly detection, and predictive maintenance.

Dataset Features

• OS Compatibility: macOS
• Data Collection Interval: 1-5 seconds
• Total Storage Limit: 4GB
• File Format: CSV & Parquet
• Data Fields:
  • timestamp: Date and time of capture
  • cpu_usage: CPU usage percentage per core
  • memory_used_mb: RAM usage in MB
  • disk_read_mb: Disk read in MB
  • disk_write_mb: Disk write in MB
  • net_sent_mb: Network upload in MB
  • net_recv_mb: Network download in MB
  • battery_status: Battery percentage (Mac only)
  • cpu_temp: CPU temperature in °C

Usage Examples

1. Load in Python

from datasets import load_dataset

dataset = load_dataset("harpertoken/harpertokenSysMon")
df = dataset["train"].to_pandas()

print(df.head())

2. Train an Anomaly Detection Model

from sklearn.ensemble import IsolationForest

# Convert time-series to numerical format
df["cpu_usage_avg"] = df["cpu_usage"].apply(lambda x: sum(x) / len(x))

# Train model
model = IsolationForest(contamination=0.05)
model.fit(df[["cpu_usage_avg", "memory_used_mb", "disk_read_mb", "disk_write_mb"]])

# Predict anomalies
df["anomaly"] = model.predict(df[["cpu_usage_avg", "memory_used_mb", "disk_read_mb", "disk_write_mb"]])

Potential Use Cases

AI Fine-Tuning for real-time system monitoring models
Time-Series Forecasting of CPU & memory usage
Anomaly Detection for overheating and system failures
Predictive Maintenance for proactive issue detection

Licensing

• License: MIT

Contact

For questions or feedback, please contact [email protected]