  The 2017 Grand Challenge focused on the problem of automatic detection of
anomalies for manufacturing equipment. This paper reports the technical details
of a solution focused on particular optimizations of the processing stages.
These included customized input parsing, fine tuning of a k-means clustering
algorithm and probability analysis using a lazy flavor of a Markov chain. We
have observed in our custom implementation that carefully tweaking these
processing stages at single node level by leveraging various data stream
characteristics can yield good performance results. We start the paper with
several observations concerning the input data stream, following with our
solution description with details on particular optimizations, and we conclude
with evaluation and a discussion of obtained results.

Performance;Data Structures and Algorithms;Software Engineering