Time-Series Data Layering for Fidelity-Based Retrieval
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Solution Overview
Problem
Existing methods for compressing time-series data often compromise accuracy and data quality, as they require data to be evenly spaced and rely on interpolation, which is not suitable for all scenarios and does not provide guarantees of statistical measures, limiting the ability to recover the original data series or query it at varying levels of fidelity.
Innovation Solution
A computer-implemented method that iteratively selects data points from a time series dataset based on multiple threshold criteria, creating layered subsets that meet specific statistical measurement criteria, allowing for stratified data access and reducing computational resources by returning only the necessary data points that meet the user-specified thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If fixed time sampling is used to compress time-series data, then the data size is reduced, but the accuracy of the data is seriously degraded
Solution Approach 1:
The patent changes the sampling parameter from fixed time intervals to variable intervals based on data characteristics. It introduces adaptive sampling rates that adjust according to the complexity and importance of data segments, allowing higher sampling rates for critical portions and lower rates for stable portions, thus maintaining accuracy while reducing overall data size
Solution Approach 2:
The patent implements dynamic sampling where the sampling rate is not static but adapts in response to data characteristics. The system continuously monitors data variability and adjusts sampling intensity accordingly, making the compression process responsive to actual data needs rather than applying a uniform reduction rate
2Quantity of substance
If interpolation methods are used to compress time-series data, then the data is stored in compressed form, but the original data cannot be recovered and accuracy is lost
Solution Approach 1:
The patent segments the time-series data into multiple layers or levels of detail, where each layer represents a different compression level. This segmentation allows the system to provide different versions of the data (from highly compressed to fully detailed) without losing the ability to recover the original, enabling progressive disclosure based on storage and bandwidth constraints
Solution Approach 2:
The patent implements a nested structure where compressed data representations are embedded within progressively more detailed representations. Each compression level contains the essential information of previous levels plus additional detail, allowing seamless transition between compression levels and full recovery of original data when needed
3Quantity of substance
If data points are removed to compress time-series data, then the subset size is reduced, but the fidelity of the data is reduced
Solution Approach 1:
The patent applies different compression strategies to different local regions of the time-series data based on their characteristics. Critical segments with high variability or importance retain more data points, while stable or less important segments undergo more aggressive compression, achieving overall reduction while preserving local fidelity where needed
Data Source
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AI summary
A computer-implemented method comprising: obtaining input data comprising a plurality of data values, wherein each data value is associated with a time value; obtaining a plurality of threshold criteria; and (a) selecting, or generating, a dataset or a plurality of datasets that are different to one another from the input data, and determining whether each selected, or generated dataset meets a threshold criterion of the plurality of threshold criteria; and (b) causing each selected, or generated, dataset that meets the threshold criterion to be stored in memory in association with the threshold criterion, repeating (a) and (b) for each threshold criterion of the plurality of threshold criteria, thus causing a plurality of datasets to be stored in the memory, wherein each stored dataset meets a threshold criterion of the plurality of threshold criteria; and wherein each stored dataset comprises a plurality of data values and an indication of the time value associated with each one of the plurality of data values.