English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

There are a prohibitively large number of floating-point time series datagenerated at an unprecedentedly high rate. An efficient, compact and losslesscompression for time series data is of great importance for a wide range ofscenarios. Most existing lossless floating-point compression methods are basedon the XOR operation, but they do not fully exploit the trailing zeros, whichusually results in an unsatisfactory compression ratio. This paper proposes anErasing-based Lossless Floating-point compression algorithm, i.e., Elf. Themain idea of Elf is to erase the last few bits (i.e., set them to zero) offloating-point values, so the XORed values are supposed to contain manytrailing zeros. The challenges of the erasing-based method are three-fold.First, how to quickly determine the erased bits? Second, how to losslesslyrecover the original data from the erased ones? Third, how to compactly encodethe erased data? Through rigorous mathematical analysis, Elf can directlydetermine the erased bits and restore the original values without losing anyprecision. To further improve the compression ratio, we propose a novelencoding strategy for the XORed values with many trailing zeros. Furthermore,observing the values in a time series usually have similar significand counts,we propose an upgraded version of Elf named Elf+ by optimizing the significandcount encoding strategy, which improves the compression ratio and reduces therunning time further. Both Elf and Elf+ work in a streaming fashion. They takeonly O(N) (where N is the length of a time series) in time and O(1) in space,and achieve a notable compression ratio with a theoretical guarantee. Extensiveexperiments using 22 datasets show the powerful performance of Elf and Elf+compared with 9 advanced competitors for both double-precision andsingle-precision floating-point values.

Erasing-based lossless compression method for streaming floating-point time series