  Over the past years, the industrial sector has seen many innovations brought
about by automation. Inherent in this automation is the installation of sensor
networks for status monitoring and data collection. One of the major challenges
in these data-rich environments is how to extract and exploit information from
these large volume of data to detect anomalies, discover patterns to reduce
downtimes and manufacturing errors, reduce energy usage, predict
faults/failures, effective maintenance schedules, etc. To address these issues,
we developed TSML. Its technology is based on using the pipeline of lightweight
filters as building blocks to process huge amount of industrial time series
data in parallel.
