Abstract: A lossy compression algorithm is described for performing data compression of high-frequency floating point time-series data, for example. The compression algorithm utilizes a prediction engine that employs at least one of a linear prediction model or a non-linear prediction model to calculate one-step-ahead prediction of a current data value at current sampling time t using N previous quantized data values, where N is the model order. A prediction error is determined between the predicted value and an actual value, and the prediction error is quantized. A quantized current data value is determined from the predicted value and the quantized prediction error. The quantized prediction error is sent from an edge device to a data decompressor on a cloud device. The decompressor reconstructs the quantized current data value using the received quantized prediction error and by generating the same predicted value as the compressor.

Abstract: Embodiments of the invention are generally directed to compressing genetic sequencing data. In many embodiments, the genetic sequencing data is reordered and encoded based on sequence homology between individual sequencing reads within the genetic sequencing data. Several embodiments are directed to systems to compress genetic sequencing data, and some embodiments are directed to non-transitory, machine-readable media that direct a processor to compress genetic sequencing data. In further embodiments, the genetic sequencing data represents paired-end sequencing data, and several embodiments transmit the data to a remote device.