Adaptive Encoding Strategies for Lossless Floating-Point Compression

Lossless floating-point time series compression is crucial for a wide range of critical scenarios, such as data transmission in the Internet of Things. In this paper, we propose a lossless floating-point compression method Elf. that employs a set of optimizations for the encodings of significand counts, leading zeros, trailing zeros and sharing conditions. Specifically, we first devise a Huffman-based method for the significand counts along with erasing flags. Then, we develop a dynamic programming algorithm with a set of pruning strategies to efficiently compute the adaptive approximation rules for leading zeros and trailing zeros, respectively. Next, we propose an adaptive sharing condition for the counts of leading zeros and trailing zeros. We further extend Elf. to Streaming Elf., i.e., SElf., which achieves almost the same compression ratio as Elf., while enjoying higher efficiency. We compare Elf. and SElf. with 9 competitors using 14 datasets, demonstrating the powerful performance of both Elf* and SElf*. All the source codes and datasets are publicly released.