Representative Data Object Compression for Redundant Data Sets
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Solution Overview
Problem
Existing computing systems face challenges with data management due to excessive data size, leading to storage and processing requirements that are difficult to manage efficiently, especially when dealing with redundant or unnecessary data.
Innovation Solution
The system employs a method to compress data by defining representative data objects for groups of data objects suitable for compression, allowing for optimal reduction in data size while maintaining valuable information, and enabling more data objects to be eligible for compression regardless of their start dates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data compression is applied to reduce data size, then storage and processing requirements are reduced, but data indicative of valuable information may be lost
Solution Approach 1:
The system extracts and removes only redundant or unnecessary data from data objects while preserving data indicative of valuable information. This is achieved through intelligent compression that identifies and eliminates duplicate or non-essential data elements, reducing overall data size without sacrificing important information.
Solution Approach 2:
The system changes parameters of data objects by applying compression transformations that modify data representation. This includes converting data to more compact forms, encoding schemes, or aggregated representations that reduce size while maintaining the essential information content through intelligent selection of what to preserve.
2Productivity
If computing resources are increased to handle larger data sets, then processing capability is improved, but hardware costs and system complexity increase
Solution Approach 1:
The system extracts and removes redundant data from data sets before processing, thereby reducing the overall volume of data that requires computational handling. This preprocessing step decreases the burden on computing resources and simplifies hardware requirements while maintaining processing capability for essential information.
3Productivity
If data is communicated between computing systems for processing, then collaborative processing is enabled, but network bandwidth is consumed and communication speed is reduced
Solution Approach 1:
The system extracts and removes redundant data from data objects before transmission between computing systems. By eliminating duplicate or unnecessary information prior to communication, the system reduces network bandwidth consumption and accelerates data transfer speeds while still enabling collaborative processing of essential information.
Data Source
AI summary
A system implements data compression for a plurality of data objects each having a respective fixed data constituent and a variable data constituent. The data compression includes selecting a first subset of the fixed data constituents and a second subset of the variable data constituents. The second subset of the variable data constituents having an end date in common and event timing in common. The system compresses the first subset of the fixed data constituents and the second subset of the variable data constituents by defining a representative data object for the fixed data constituent subset and the variable data constituent subset.