Time-Series Motif Compression for Low-Overhead Pattern Analysis
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Solution Overview
Problem
The increasing complexity and data volume of time-series data in computer systems make it difficult for administrators and analytics teams to identify patterns, anomalies, and trends, especially when data is shared with third parties who may not have access to all environmental data, leading to inefficiencies in CPU utilization and resource management.
Innovation Solution
A system that uses unsupervised machine learning to cluster time-series data into motifs, reducing data storage and analysis burden by compressing datasets while maintaining accuracy, allowing for efficient CPU utilization and forecasting future behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If time-series data is stored and analyzed in its original format, then complete information is preserved for analysis, but data volume and processing complexity increase significantly
Solution Approach 1:
The time-series data is segmented into repeating patterns called motifs. The system identifies and groups similar contiguous subsequences into motif clusters, representing large portions of the original data with compact motif signatures. This segmentation reduces data volume while preserving essential patterns for analysis.
Solution Approach 2:
Instead of storing and processing the complete original time-series data, the system creates compressed representations by copying and storing only the unique motif patterns. These motif copies serve as proxies for the full dataset, enabling analysis with significantly reduced data volume while maintaining pattern recognition accuracy.
2Loss of information
If complete environmental data is shared with third parties for analysis, then comprehensive insights can be obtained, but data transmission and processing overhead increase
Solution Approach 1:
The system extracts only the essential pattern information from the complete environmental data by identifying motifs. Instead of transmitting all raw time-series data to third parties, only the compressed motif representations are shared, containing the critical patterns needed for analysis while excluding redundant information.
Solution Approach 2:
The data representation is transformed from raw time-series values to motif-based parameters. This parameter change compresses the data structure while preserving the meaningful patterns, enabling efficient transmission and processing by third parties without losing analytical value.
3Measurement precision
If large volumes of time-series data are processed, then detailed analysis is possible, but CPU utilization and computing resources are consumed
Solution Approach 1:
The system segments the large volume of time-series data into manageable motif units. By organizing data into repeating pattern clusters, the system enables analysis at the motif level rather than processing every individual data point, significantly reducing CPU utilization while maintaining analytical detail through the preserved pattern structures.
Data Source
AI summary
Systems and methods are provided for compressing a time-series dataset from a monitored device into a compressed dataset representation. Using an unsupervised machine learning model, the system may group a contiguous set of datapoints of the time-series dataset and group, using a distance algorithm, the first cluster to a first motif. A compressed dataset representation can be generated using a plurality of motifs, including the first motif, that is stored in place of the time-series dataset. This can allow the time-series dataset to be replaced with the compressed dataset representation, illustrating an overall, abstracted definition of the time-series dataset rather than the individual data points.