  Piecewise Linear Approximation (PLA) is a well-established tool to reduce the
size of the representation of time series by approximating the series by a
sequence of line segments while keeping the error introduced by the
approximation within some predetermined threshold. With the recent rise of edge
computing, PLA algorithms find a complete new set of applications with the
emphasis on reducing the volume of streamed data. In this study, we identify
two scenarios set in a data-stream processing context: data reduction in sensor
transmissions and datacenter storage. In connection to those scenarios, we
identify several streaming metrics and propose streaming protocols as
algorithmic implementations of several state of the art PLA techniques. In an
experimental evaluation, we measure the quality of the reviewed methods and
protocols and evaluate their performance against those streaming statistics.
All known methods have deficiencies when it comes to handling streaming-like
data, e.g. inflation of the input stream, high latency or poor average error.
Our experimental results highlight the challenges raised when transferring
those classical methods into the stream processing world and present
alternative techniques to overcome them and balance the related trade-offs.
