Lossy Data Compression via Key Artifact Extraction
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Solution Overview
Problem
Existing data compression methods fail to effectively separate noisy data from essential data, leading to inefficient resource utilization and suboptimal visualization of compressed data streams, particularly in multidimensional data processing where high signal-to-noise ratios are common.
Innovation Solution
A method for compressing data streams into cycle metrics by identifying key artifacts such as peak, trough, and breakeven points, and calculating percentage differences between these, allowing for the filtering out of noisy data and highlighting relevant information, which can be processed and visualized efficiently across various resolutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If lossy data compression is applied to reduce data size, then data transmission and storage efficiency improve, but data quality and signal fidelity deteriorate
Solution Approach 1:
The patent extracts and removes noisy data points from the data stream while preserving key artifacts (peaks, troughs, breakeven points). By identifying and eliminating only the irrelevant noisy portions rather than uniformly compressing all data, the system reduces data size while maintaining signal fidelity of the essential components.
Solution Approach 2:
The patent applies different processing quality to different parts of the data stream. Key artifacts (peaks, troughs, breakeven points) are preserved with high fidelity, while noisy intermediate points are filtered out or compressed more aggressively. This local differentiation of quality allows efficient compression without losing important signal information.
2Loss of information
If detailed data is preserved in compression schemes, then data quality improves, but CPU utilization and processing efficiency worsen
Solution Approach 1:
The patent extracts only the essential key artifacts (peaks, troughs, breakeven points) from the complete data stream, discarding the noisy intermediate data. This extraction approach maintains data quality for critical points while dramatically reducing the total data volume that requires CPU processing, storage, and transmission.
Solution Approach 2:
The patent segments the continuous data stream into discrete key artifacts and noisy portions, processing them differently. By segmenting the data into essential vs. non-essential components, the system can apply efficient compression to the essential segments while ignoring or minimally processing the non-essential noisy segments, improving overall CPU efficiency.
3Loss of information
If all data points are processed and visualized, then complete information is provided, but visualization clarity and key point identification worsen
Solution Approach 1:
The patent extracts and highlights only the key artifacts (peaks, troughs, breakeven points) from the data stream for visualization purposes. By removing noisy intermediate points from the visualization layer while preserving the underlying complete data, the system maintains information completeness in the data model while achieving clarity in the visual representation.
Solution Approach 2:
The patent applies different visualization quality to different data points. Key artifacts are rendered with high visibility and emphasis, while noisy data points are either omitted or rendered with low visibility. This local differentiation of visualization quality allows the display to convey complete information hierarchically while maintaining clarity for key insights.
Data Source
AI summary
Systems and methods for lossy data compression using key artifacts and dynamically generated cycles are described, including receiving a data stream of relational data; detecting an artifact in the data stream associated with a critical point in the relational data; analyzing, by a processing device, the artifact to determine a set of artifacts associated with a complex event where the critical point is an endpoint of the complex event; calculating one or more cycle metrics based on the set of artifacts; generating a data structure associated with the complex event and cycle metrics; providing the data structure for marking a display of the relational data with the complex event based on the cycle metrics.