Anomaly detection and motif discovery in symbolic representations of
  time series
  The advent of the Big Data hype and the consistent recollection of event logs
and real-time data from sensors, monitoring software and machine configuration
has generated a huge amount of time-varying data in about every sector of the
industry. Rule-based processing of such data has ceased to be relevant in many
scenarios where anomaly detection and pattern mining have to be entirely
accomplished by the machine. Since the early 2000s, the de-facto standard for
representing time series has been the Symbolic Aggregate approXimation (SAX).In
this document, we present a few algorithms using this representation for
anomaly detection and motif discovery, also known as pattern mining, in such
data. We propose a benchmark of anomaly detection algorithms using data from
Cloud monitoring software.
