Prime Data Sieve for Fast Retrieval of Losslessly Reduced Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data compression techniques struggle to efficiently uncover and exploit redundancy across large and extremely large datasets, leading to limitations in data ingestion and retrieval rates, especially when data is stored across multiple tiers of storage hierarchy, and they are not suited for random access or require excessive computational resources.
Innovation Solution
The method involves breaking down input data into Prime Data Elements and Derivative Elements, using a Prime Data Sieve to organize and store these elements, allowing for content-associative access and deriving reduced representations through a Reconstitution Program, thereby achieving lossless data reduction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is stored in compressed form using existing compression techniques, then data footprint is reduced, but retrieval speed and ingestion rate deteriorate
Solution Approach 1:
The patent segments data into prime data elements and derivative elements, organizing them in a sieve structure that enables selective access. This segmentation allows the system to retrieve only necessary data portions without decompressing entire datasets, thereby maintaining small footprint while improving retrieval speed.
Solution Approach 2:
The patent introduces an intermediary decompression mechanism that operates on compressed data without requiring full decompression. This intermediary layer enables selective retrieval and processing of compressed data, resolving the contradiction between maintaining compressed footprint and achieving fast retrieval.
2Productivity
If data is stored in uncompressed form for fast access, then retrieval speed is improved, but storage cost and data footprint increase
Solution Approach 1:
The patent applies local quality by maintaining different data representations in different locations within the sieve structure. Frequently accessed data portions are kept in optimized formats while less accessed data remains compressed, achieving fast retrieval where needed without paying storage costs for all data.
3Quantity of substance
If existing compression techniques are applied to large datasets, then data reduction is achieved, but computational resources and processing time increase excessively
Solution Approach 1:
The patent performs preliminary organization of data into prime and derivative elements during the compression phase, creating a structured sieve that enables efficient retrieval without requiring intensive computational resources during access. This preliminary structuring reduces the computational burden of subsequent operations.
4Quantity of substance
If data is organized in traditional storage hierarchy, then storage capacity is optimized, but access efficiency for diverse data types deteriorates
Solution Approach 1:
The patent creates a universal sieve structure that can handle diverse data types and access patterns through a unified interface. This multi-functional structure maintains storage capacity optimization while improving access efficiency for various data types through consistent organization principles.
Data Source
Figure 1A
Figure 1B
Figure 1C
AI summary
An amount of memory needed to hold prime data elements during reconstitution may be determined by examining the creation and usage of prime data elements and their spatial and temporal characteristics during data distillation.