Sprintz: Time Series Compression for the Internet of Things (1808.02515v1)

Abstract: Thanks to the rapid proliferation of connected devices, sensor-generated time series constitute a large and growing portion of the world's data. Often, this data is collected from distributed, resource-constrained devices and centralized at one or more servers. A key challenge in this setup is reducing the size of the transmitted data without sacrificing its quality. Lower quality reduces the data's utility, but smaller size enables both reduced network and storage costs at the servers and reduced power consumption in sensing devices. A natural solution is to compress the data at the sensing devices. Unfortunately, existing compression algorithms either violate the memory and latency constraints common for these devices or, as we show experimentally, perform poorly on sensor-generated time series. We introduce a time series compression algorithm that achieves state-of-the-art compression ratios while requiring less than 1KB of memory and adding virtually no latency. This method is suitable not only for low-power devices collecting data, but also for servers storing and querying data; in the latter context, it can decompress at over 3GB/s in a single thread, even faster than many algorithms with much lower compression ratios. A key component of our method is a high-speed forecasting algorithm that can be trained online and significantly outperforms alternatives such as delta coding. Extensive experiments on datasets from many domains show that these results hold not only for sensor data but also across a wide array of other time series.

• The paper presents Sprintz, a compression algorithm that integrates forecasting, bit packing, run-length, and entropy coding to efficiently compress IoT time series data.
• It leverages the innovative Fire method, an online learning predictive model that significantly improves prediction accuracy and overall compression efficiency.
• Extensive experiments demonstrate that Sprintz outperforms current methods with high compression ratios and decompression speeds reaching up to 3GB/s.

Insightful Overview of "Sprintz: Time Series Compression for the Internet of Things"

The paper "Sprintz: Time Series Compression for the Internet of Things," authored by Davis Blalock, Samuel Madden, and John Guttag, addresses a significant challenge in handling large-scale sensor-generated time series data in the Internet of Things (IoT): efficient compression. The authors propose Sprintz, a novel compression algorithm optimized for time series data that combines high compression ratios with minimal memory footprint and negligible latency, targeting resource-constrained environments typical of IoT devices.

Summary of the Method

Sprintz is a bit packing-based predictive coder tailored for multivariate integer time series. It encompasses four primary components: forecasting, bit packing, run-length encoding, and entropy coding. The forecasting component can utilize either traditional delta coding or a newly introduced method, Fire (Fast Integer Regression). Fire notably improves compression efficiency through an online learning strategy that adjusts prediction coefficients based on the data's temporal characteristics.

In the bit packing stage, Sprintz reduces the size of prediction errors by exploiting the correlations among successive samples. It optimizes bit usage by encoding minimum necessary bits for each column of data after zigzag encoding. When data shows no change, run-length encoding efficiently represents consecutive zero-difference blocks, markedly enhancing the compression ratio. Finally, Huffman coding is applied to further encode the packed data, effectively reducing redundancy by assigning shorter codes to more frequent values.

Strong Numerical Results and Comparative Analysis

The extensive experimental evaluation showcases Sprintz's competitive edge over existing methods. Notably, on the UCR Time Series Archive datasets, Sprintz consistently achieves higher compression ratios compared to algorithms like SIMD-BP128, FastPFOR, Snappy, and even contemporary general-purpose compression frameworks such as Zstd and LZ4. This capability is reinforced by statistically significant results from Nemenyi tests, evidencing Sprintz's superior performance.

Sprintz demonstrates exceptional decompression speeds, comfortably exceeding 500MB/s when leveraging Huffman coding and reaching up to 3GB/s for decompression in single-thread executions without entropy coding. Furthermore, it delivers robust performance under varying conditions of compressibility, making it highly adaptable to different time series characteristics inherent in IoT data streams.

Practical and Theoretical Implications

Practically, Sprintz's compression efficiency implies lower power consumption during data transmission and reduced storage costs in centralized servers, aligning with IoT devices' operational constraints. By minimizing the computational demands on resource-limited devices, Sprintz enables prolonged device autonomy and enhances real-time data analytics capabilities.

Theoretically, Sprintz extends the discourse on time series data compression by integrating an adaptive forecasting model capable of real-time learning. The Fire algorithm, with its efficient adaptation mechanism, represents an innovative step in predictive modeling for compression, opening avenues for further exploration in dynamic and context-aware compression algorithms.

Future Prospects in AI and Compression

Beyond immediate applications, the approach presented in Sprintz highlights the potential for AI-driven compression techniques to transform data handling in distributed sensor networks. As data explosion continues across IoT domains, embedding intelligent compression methods that leverage the context and inherent patterns of time series data is likely to become more critical.

The prospects for such advancements are vast, ranging from more refined predictive models that could adapt to more complex data structures to enhanced algorithms that seamlessly integrate with edge AI platforms. Such developments could significantly optimize data flows in smart cities, autonomous systems, and pervasive computing environments.

In conclusion, the paper demonstrates how Sprintz stands as a resilient solution capable of tackling the dual challenge of efficiency and resource limitations in IoT time series compression. With its promising results and implications, Sprintz is poised to influence future research and applications in data-intensive domains.