Logging is a common practice in software engineering to provide insights into working
systems. The main uses of log files have always been failure identification and root cause …

Data-driven problem solving in many real-world applications involves analysis of time-
dependent multivariate data, for which dimensionality reduction (DR) methods are often …

Software logs record system activities, aiding maintainers in identifying the underlying
causes for failures and enabling prompt mitigation actions. However, maintainers need to …

Logs play an important part in analyzing system behavior and diagnosing system failures.
As the basic step of log analysis, log parsing converts raw log messages into structured log …

With the rapid accumulation of text data produced by data-driven techniques, the task of
extracting" data annotations"—concise, high-quality data summaries from unstructured raw …

The emergence of large-scale AI models, like GPT-4, has significantly impacted academia
and industry, driving the demand for high-performance computing (HPC) to accelerate …

Analysis of large, high-dimensional, and heterogeneous datasets is challenging as no one
technique is suitable for visualizing and clustering such data in order to make sense of the …

Many real-world applications involve analyzing time-dependent phenomena, which are
intrinsically functional, consisting of curves varying over a continuum (eg, time). When …

The ability to monitor and interpret hardware system events and behaviors is crucial to
improving the robustness and reliability of these systems, especially in a supercomputing …

To maintain a robust and reliable supercomputing facility, monitoring it and understanding
its hardware system events and behaviors is an essential task. Exascale systems will be …