Data Compression Patterns for Analytics Processing
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Solution Overview
Problem
Current data analytics techniques are inefficient for processing large volumes of data, requiring significant expertise and time to identify correlations, especially in big data formats like matrices with millions of columns and billions of rows, and are limited by the need to analyze raw data in paper formats.
Innovation Solution
A system comprising processing circuitry and memory that compresses data into patterns summarizing information, allowing for efficient data transfer, processing, and storage, enabling the application of advanced analytics without needing to analyze individual data points, and using these patterns to apply rules for decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data is stored and processed in raw format with millions of columns and billions of rows, then complete information is preserved, but processing time and computational resources increase significantly
Solution Approach 1:
The patent extracts only the essential patterns and correlations from the raw data, storing compressed representations that capture the most important information. This allows the system to work with condensed data structures that retain critical insights while eliminating redundant information, thereby reducing processing time without significant loss of analytical value
Solution Approach 2:
The system performs preliminary data compression and pattern extraction before the actual analytics processing. By pre-processing the data into compressed formats with embedded patterns, the system prepares the data in advance for faster querying and analysis, avoiding the need to process raw data structures during runtime
2Measurement precision
If raw data in paper formats is analyzed to identify correlations, then accurate insights can be obtained, but extensive expertise and time are required
Solution Approach 1:
The system automatically performs pattern extraction and correlation identification without requiring manual analysis. The compression process itself generates patterns that reveal correlations, enabling the system to self-analyze the data and provide insights without extensive human expertise or complex manual processing procedures
Solution Approach 2:
The patent transforms the data from raw numerical values into compressed patterns with different parameter representations. This parameter transformation changes the data structure into a form that naturally reveals correlations and relationships, making the analysis process simpler and more automated while maintaining accuracy
3Loss of information
If all individual data values are stored, then complete data availability is maintained, but storage efficiency and transfer speed decrease
Solution Approach 1:
The patent merges multiple individual data values into compressed pattern representations that encode the essential information from many data points. This consolidation reduces the total volume of data that needs to be stored and transferred, while the patterns preserve the critical relationships and correlations needed for analytics
Solution Approach 2:
Instead of storing and transferring all original data values, the system creates compressed copies or representations that capture the essential patterns. These compressed copies are sufficient for most analytics operations, enabling efficient storage and fast transfer while maintaining the ability to derive meaningful insights
Data Source
AI summary
In certain embodiments, a system comprises a memory operable to maintain a plurality of profiles, an interface operable to receive data comprising a plurality of values, and processing circuitry. The processing circuitry is operable to compress the plurality of values into one or more patterns that summarize information about the plurality of values without storing each of the plurality of values in the one or more patterns. Each pattern is associated with a respective profile of the plurality of profiles based on a relationship between the respective profile and the values used to determine each pattern. The processing circuitry is further operable to determine to apply a rule to a first profile of the plurality of profiles, apply the rule to the one or more patterns associated with the first profile, and communicate a result of applying the rule.