This research introduces a novel time curves visualization approach for analyzing massive software logs, maintaining temporal and relational context that clustering algorithms typically lose.

• Overcomes the chaotic nature of logs (high volume, lack of standards, variability)
• Preserves critical temporal patterns essential for security threat detection
• Enables more effective identification of security risks in large-scale systems
• Complements clustering algorithms with visual analytics for comprehensive log monitoring

For security teams, this approach transforms impenetrable log data into interpretable visual patterns, allowing faster detection of anomalies and potential security breaches in complex software environments.

Analyzing Logs of Large-Scale Software Systems using Time Curves Visualization