Table Content Compression Using Byte-to-Integer Transformation
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Solution Overview
Problem
Current lossy compression methods for content, such as alphanumeric text in tables, are inefficient in processing time and memory usage, especially in distributed computing environments like Hadoop systems, where rapid content delivery is crucial for user engagement and advertising effectiveness.
Innovation Solution
Implementing a lossy compression approach that transforms table entries into multiple-precision integers using ASCII equivalent byte values and entropy encoding, reducing memory consumption by 25-50% while allowing for faster access and processing of compressed tables linked with user identification parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If lossy compression is used to reduce memory consumption and processing time, then compression speed and memory efficiency improve, but content accuracy and lossless reconstruction capability deteriorate
Solution Approach 1:
The patent transforms the content representation by changing parameters - converting alphanumeric text into numerical formats (ASCII values, then multiple-precision integers). This parameter transformation enables more efficient compression while maintaining sufficient accuracy for the intended use case of advertising content delivery
Solution Approach 2:
The patent accepts that the compressed content is a simplified approximation rather than an exact replica. The multiple-precision integer representation is a 'cheaper' form that sacrifices some precision but provides adequate functionality for rapid content delivery in distributed computing environments
2Quantity of substance
If traditional lossy compression methods are used, then memory consumption is reduced, but processing time remains excessive for distributed computing environments
Solution Approach 1:
The patent segments the compression process into distinct stages: converting text to ASCII byte values, then to multiple-precision integers. This segmentation allows each stage to be optimized independently, achieving both memory efficiency and processing speed
Solution Approach 2:
By changing the representation parameters from characters to numerical values, the patent enables more compact storage and faster processing operations, directly addressing both memory consumption and processing time concerns
3Productivity
If content is compressed for faster delivery, then user engagement improves, but computational resources and memory usage increase
Solution Approach 1:
The patent performs compression transformations in advance - converting content to multiple-precision integers before storage or transmission. This preliminary action reduces the computational burden during content delivery, as the compressed form requires fewer resources to process and transmit
Solution Approach 2:
The parameter transformation to numerical representations enables more efficient computational operations during delivery, reducing the energy and computational resources required while maintaining fast content delivery speed
Data Source
AI summary
Briefly, methods and/or systems of processing content entries are described. An example may comprise determining equivalent byte values of characters that form the content entries. The content entries may be transformed based, at least in part, on the equivalent byte values and compressed using, for example, delta compression.