  Modern High-Performance Computing (HPC) and data center operators rely more
and more on data analytics techniques to improve the efficiency and reliability
of their operations. They employ models that ingest time-series monitoring
sensor data and transform it into actionable knowledge for system tuning: a
process known as Operational Data Analytics (ODA). However, monitoring data has
a high dimensionality, is hardware-dependent and difficult to interpret. This,
coupled with the strict requirements of ODA, makes most traditional data mining
methods impractical and in turn renders this type of data cumbersome to
process. Most current ODA solutions use ad-hoc processing methods that are not
generic, are sensible to the sensors' features and are not fit for
visualization.
  In this paper we propose a novel method, called Correlation-wise Smoothing
(CS), to extract descriptive signatures from time-series monitoring data in a
generic and lightweight way. Our CS method exploits correlations between data
dimensions to form groups and produces image-like signatures that can be easily
manipulated, visualized and compared. We evaluate the CS method on HPC-ODA, a
collection of datasets that we release with this work, and show that it leads
to the same performance as most state-of-the-art methods while producing
signatures that are up to ten times smaller and up to ten times faster, while
gaining visualizability, portability across systems and clear scaling
properties.
