Adaptive Data Compression for Memory and Bandwidth Constraints
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Solution Overview
Problem
Organizations face challenges in managing memory storage and communication bandwidth due to increasing computing resources demands, particularly in financial institutions handling large volumes of electronic transactions, which can lead to memory depletion and communication slowdowns.
Innovation Solution
Implementing a system that compresses data based on stored information by monitoring data structure sizes and applying compression parameters to reduce memory usage and communication bandwidth requirements, thereby minimizing the need for additional storage devices and reducing communication delays.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is stored in uncompressed format to maintain data integrity and accessibility, then data quality and reliability are improved, but memory storage capacity is depleted faster
Solution Approach 1:
The patent applies parameter changes by transitioning data from an uncompressed state to a compressed state using compression algorithms. The system dynamically adjusts compression parameters based on data characteristics, achieving significant reduction in storage capacity consumption while maintaining data integrity through reversible compression processes that preserve all original information.
2Productivity
If data structure size is increased to store more electronic transactions, then productivity and data retention are improved, but communication bandwidth is consumed faster
Solution Approach 1:
The patent transforms data structures from uncompressed to compressed formats, dynamically adjusting compression parameters based on data characteristics. This enables the system to retain more electronic transactions within the same communication bandwidth constraints, effectively increasing productivity without proportionally increasing bandwidth consumption.
3Quantity of substance
If compression algorithms are applied to reduce memory usage, then storage capacity efficiency is improved, but computing processing power is consumed
Solution Approach 1:
The patent implements partial compression by selectively applying compression algorithms only to data structures that meet specific size thresholds or criteria. Rather than compressing all data uniformly, the system identifies and compresses only those data structures where compression is beneficial, reducing overall computing processing power consumption while maintaining effective storage efficiency improvements.
Solution Approach 2:
The system employs adaptive compression that automatically monitors data structure sizes and applies compression only when beneficial, making the compression process self-regulating. The data structures essentially 'self-manage' their own compression state based on their current size and characteristics, reducing the need for continuous external processing power intervention.
4Reliability
If data structures are synchronized in real-time between computing systems, then data reliability and accessibility are improved, but communication delays increase
Solution Approach 1:
The patent applies compression to data structures before synchronization, fundamentally changing the data size parameter. This compression reduces the amount of data that needs to be transmitted during real-time synchronization between computing systems, thereby maintaining data accessibility and reliability while significantly reducing communication delays and bandwidth consumption.
Data Source
AI summary
Systems, devices and methods for adaptive compression of stored information includes a memory management computing device programmed to monitor a size of a plurality of data structures stored in a data repository. The computing device compares the size of each of a plurality of data structures to a predetermined threshold. When a size of an uncompressed data structure meets the threshold, the memory management computing device calculates a value of a first compression parameter based on a value of a first parameter and a value of a second parameter of each data element of the uncompressed data structure, calculates a value of a second compression parameter based the value of the first parameter of each data element of the uncompressed data structure, generates a compressed data structure based on the value of the first compression parameter and the second compression parameter; and replaces, in the data repository, the uncompressed data structure with the compressed data structure.