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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.
Many separable nonlinear optimization problems can be approximated by their nonlinear objective functions with piecewise linear functions. A natural question arising from applying this approach is how to break the interval of interest into subinterva
Motivated by the high-frequency data streams continuously generated, real-time learning is becoming increasingly important. These data streams should be processed sequentially with the property that the stream may change over time. In this streaming
X-ray scattering experiments using Free Electron Lasers (XFELs) are a powerful tool to determine the molecular structure and function of unknown samples (such as COVID-19 viral proteins). XFEL experiments are a challenge to computing in two ways: i)
Most existing interpretable methods explain a black-box model in a post-hoc manner, which uses simpler models or data analysis techniques to interpret the predictions after the model is learned. However, they (a) may derive contradictory explanations
Data streaming relies on continuous queries to process unbounded streams of data in a real-time fashion. It is commonly demanding in computation capacity, given that the relevant applications involve very large volumes of data. Data structures act as