Telemetry Data Compression Using Autoregressors for Low-Error Prediction
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Solution Overview
Problem
Prediction models associated with distributed data sources face challenges in maintaining accuracy due to the need for frequent updates and high bandwidth consumption, as existing compression methods either result in low compression ratios or unacceptable prediction errors.
Innovation Solution
A compression/decompression framework using an autoregressor that learns to compress and decompress data specifically for prediction tasks, leveraging machine learning to identify patterns in telemetry data and achieve high compression ratios while maintaining low prediction errors, with an AI engine that retrains the model based on prediction errors and data availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If lossless compression algorithms are used, then prediction accuracy is maintained, but compression ratio is limited to around 70% and compression time is long (around 1 minute for 1 million data points)
Solution Approach 1:
The patent transitions from lossless compression to lossy compression, changing the fundamental parameter of data fidelity. This allows achieving compression ratios of 90% or higher while controlling prediction error through careful model design and hyperparameter optimization, thereby resolving the contradiction between compression speed and prediction accuracy.
Solution Approach 2:
The patent replaces traditional mechanical compression algorithms with machine learning-based compression models. These ML models learn patterns in the data and can achieve both high compression ratios and acceptable prediction accuracy by predicting and reconstructing only the most important features, thus improving compression speed without completely sacrificing accuracy.
2Productivity
If lossy compression is used to achieve higher compression ratios, then compression speed improves, but prediction error rate increases
Solution Approach 1:
The patent applies partial compression by selectively compressing only certain features or dimensions of the data that are less critical for prediction accuracy. This allows achieving high overall compression ratios while preserving the most important information needed for accurate predictions, thus balancing compression speed and error rate.
Solution Approach 2:
The patent implements feedback mechanisms where the compression model is trained and evaluated based on prediction error metrics. The model parameters and compression strategies are adjusted based on feedback from validation sets, allowing the system to optimize the trade-off between compression ratio and prediction error rate iteratively.
3Measurement precision
If data is frequently transmitted to centralized infrastructure for model updates, then prediction model accuracy is maintained, but bandwidth consumption increases substantially
Solution Approach 1:
The patent segments the compression and prediction tasks to be performed locally at distributed data sources using edge devices or local servers. Only compressed results or selectively transmitted features are sent to the centralized infrastructure, dramatically reducing bandwidth consumption while maintaining model accuracy through periodic updates and distributed training approaches.
Solution Approach 2:
The patent enables local systems to perform self-service compression and preliminary prediction tasks using deployed ML models. This reduces the need for frequent data transmission to centralized infrastructure, as local systems can handle routine compression and prediction independently, only contacting the central system when model updates are required.
Data Source
AI summary
An autoregressor that compresses input data for a specific purpose. Input data is compressed using a compression/decompression framework and by accounting for a purpose of a prediction model. The compression aspect of the framework is distributed and the decompression aspect of the framework may be centralized. The compression/decompression framework and a machine learning prediction model can be centrally trained. The compressor is distributed to nodes such that the input data can be compressed and transmitted to a central node. The model and the compression/decompression framework are continually trained on new data. This allows for lossy compression and higher compression rates while maintaining low prediction error rates.