Pongo: Efficient Lossless Floating Point Compression

A large amount of time series data is increasingly being collected in different fields. In order to make good use of this large amount of time series data, it is necessary to solve the problems of high storage costs and transmission bandwidth that the data bring. The general compression algorithms effectively reduce the size of data at the cost of a large amount of computation. However, due to the huge time cost and batch processing mode of the general compression algorithms, Time Series Management Systems (TSMSs) often use streaming compression algorithms to replace general compression algorithms for compressing time series data. For floating-point data, most prevalent streaming compression algorithms, such as those based on exclusive OR (XOR) operations, offer relatively fast processing and high compression ratios compared to conventional generalpurpose compression algorithms. Among them, the Elf algorithm proposes the idea of first erasing and then compressing, achieving the best compression ratio among existing streaming compression algorithms. This paper proposes a new lossless streaming compression algorithm Pongo for floating-point numbers, which uses a carefully designed er