Change a Bit to save Bytes: Compression for Floating Point Time-Series Data (2303.04478v1)

Abstract: The number of IoT devices is expected to continue its dramatic growth in the coming years and, with it, a growth in the amount of data to be transmitted, processed and stored. Compression techniques that support analytics directly on the compressed data could pave the way for systems to scale efficiently to these growing demands. This paper proposes two novel methods for preprocessing a stream of floating point data to improve the compression capabilities of various IoT data compressors. In particular, these techniques are shown to be helpful with recent compressors that allow for random access and analytics while maintaining good compression. Our techniques improve compression with reductions up to 80% when allowing for at most 1% of recovery error.

• The paper presents two novel preprocessing methods—addition and multiplication transforms—that enhance compression of floating point time-series data.
• The techniques align data mantissas to achieve up to 80% size reduction while preserving accuracy within a 1% recovery error margin.
• Extensive evaluations across multiple compressors demonstrate that these transforms can substantially cut transmission costs and storage requirements for IoT applications.

Analyzing Compression Techniques for Floating Point Time-Series Data

The exponential growth in the number of IoT devices has necessitated the efficient management of the large volumes of data they generate. Central to this requirement is the need for effective compression techniques that not only reduce the data volume but also support direct analytics on compressed data. The paper "Change a Bit to save Bytes: Compression for Floating Point Time-Series Data" investigates two novel preprocessing techniques aimed at enhancing the compression capabilities of existing IoT data compressors. These techniques, known as the addition transform and multiplication transform, focus on preprocessing a stream of floating point data to improve compression efficiency.

Overview of Proposed Techniques

The paper introduces two preprocessing methods that act on individual data samples to enhance subsequent compression:

1. Addition Transform: This method involves shifting all data samples in the dataset by an addition parameter, $A$. This shift aims to make data samples share more bits in their mantissa by moving them to a suitable region on the real axis, thus increasing their compressibility. Data recovery is achieved via the inverse transformation, with the shifted value $A$ stored as metadata.
2. Multiplication Transform: This method involves an intricate substitution of the original data samples with approximations. These approximations, when multiplied by a specific value $M$, result in sequences with many common ending zeros in their mantissa. This technique exploits known patterns in floating point numbers to maximize shared bits, thereby improving compression.

Numerical Results and Performance Evaluation

Empirical results demonstrate the effectiveness of these proposed techniques in terms of compression ratio (CR) improvement under various error bounds. Notably, both the addition and multiplication transforms offer compression reductions of up to 80% when allowing a maximum recovery error of 1%. This performance is juxtaposed against a baseline without preprocessing and other preprocessing methods, showing significant improvements with the proposed techniques.

The paper provides comprehensive evaluations using different datasets and a variety of compressors such as Greedy-GD, bzip2, LZ4, and Zstandard. The evaluations consistently demonstrate that the preprocessing methods offer superior compression performance compared to unprocessed data and other existing methods.

Implications and Speculation on Future Developments

The findings of this paper have both practical and theoretical implications. Practically, these preprocessing techniques can be integrated into existing IoT data compression pipelines to enhance their efficiency, potentially leading to notable reductions in data transmission costs and storage requirements. Theoretically, this research underscores the importance of considering the structure and representation of data when designing compression algorithms, particularly for floating point numbers.

Speculation on future developments in AI may lead to the exploration of adaptive preprocessing methods that autonomously select the optimal transform parameters based on incoming data characteristics and desired error bounds. Furthermore, the principles demonstrated here could extend to other data types beyond time-series, broadening the scope and applicability of such preprocessing techniques in data compression fields.

In summary, the proposed techniques in this paper reflect a valuable step forward in efficiently managing the burgeoning data from IoT devices through sophisticated preprocessing strategies. By enhancing data compressibility, these methods can substantially aid in both storage reduction and maintaining the integrity of analytics performed on compressed data.