Representative Data Objects for Lossless Compression Accrual
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data management systems face challenges in efficiently compressing large sets of data objects due to redundancy and resource constraints, leading to increased storage and processing demands, while indiscriminate deletion results in information loss.
Innovation Solution
The method involves defining representative data objects within groups suitable for compression, allowing for intelligent reduction of data object sets by grouping data objects with different start dates and maintaining information integrity through accrual of historical components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data compression is applied to reduce storage and processing demands, then storage and processing loads are reduced, but information loss occurs due to indiscriminate deletion of data objects
Solution Approach 1:
The patent changes the parameter of data object representation by introducing representative data objects that aggregate multiple individual data objects. Instead of deleting individual data objects, the system transforms them into a condensed representative form that preserves essential information while reducing the total number of objects stored and processed.
Solution Approach 2:
The patent creates representative data objects that serve as copies or summaries of groups of individual data objects. These representative objects contain aggregated information that captures the essential characteristics of the original data set without requiring storage of every individual data object, thus reducing storage demands while preserving information integrity.
2Productivity
If computing resources are increased to handle larger data sets, then processing capability is improved, but hardware costs and resource constraints worsen due to budgetary limitations
Solution Approach 1:
The patent extracts and removes redundant or duplicate data objects from the data set, keeping only representative instances. This extraction process reduces the total quantity of data objects that need to be processed, thereby decreasing the computing resources required while maintaining the essential information content needed for processing tasks.
Solution Approach 2:
The patent merges multiple individual data objects into single representative data objects that encapsulate the essential information from the group. This combining process reduces the total number of objects requiring processing operations, thereby improving processing capability while reducing the quantity of computing resources needed.
3Ease of operation
If network communication is increased to transfer more data objects, then data accessibility is improved, but network bandwidth is depleted due to finite communication resources
Solution Approach 1:
The patent changes the parameter of data transmission by transferring representative data objects instead of individual data objects. This parameter change reduces the volume of network traffic required to achieve the same data accessibility goals, as representative objects contain aggregated information that represents multiple individual objects in a condensed form.
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.