Scalable Shapeoid Recognition on Multivariate Data Streams with Apache Beam

Time series representation and discretisation methods are susceptible to scaling over massive data streams. A recent approach for transferring time series data to the realm of symbols under primitives, named shapeoids has emerged in the area of data mining and pattern recognition. A shapeoid will characterise a subset of the time series curve in words from its morphology. Data processing frameworks are typical examples for running operations on top of fast unbounded data, with innate traits to enable other methods which are restricted to bounded data. Apache Beam is emerging with a unified programming model for streaming applications able to uniquely translate and run on multiple execution engines, saving development time to focus on other design decisions. We develop an application on Apache Beam which transfers the concept of shapeoids to a scenario in large-scale network flow monitoring infrastructure and evaluate it over two stream computing engines.