Third-Party Data Aggregation With Standardized Compression
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Solution Overview
Problem
Existing data aggregation systems fail to efficiently combine and utilize data from multiple sources due to high operational strain on system resources and lack of standardized data formatting and compression, hindering effective analysis and utilization.
Innovation Solution
A computing system and method that queries third-party databases with reduced operational strain, standardizes data formatting, compresses data by encoding to reduce storage size, and classifies data with metadata labeling and indexing rules to facilitate prioritization and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data is aggregated from multiple third-party sources to create comprehensive datasets, then data completeness and analytical value are improved, but system resource strain and operational complexity increase
Solution Approach 1:
The system performs data aggregation and compression operations during off-peak hours (predetermined times with less operational strain) before analysis is needed. This preliminary action reduces the computational burden during peak operational periods, resolving the contradiction between data completeness and system resource strain.
Solution Approach 2:
The system extracts only the essential and relevant data elements from third-party sources, filtering out redundant information. This extraction approach maintains data completeness for analysis while reducing the overall volume of data that requires storage and processing, thereby lowering system resource strain.
2Reliability
If raw data from multiple sources is stored without compression, then data integrity is maintained, but storage space requirements increase
Solution Approach 1:
The system changes the parameter of data representation by applying compression algorithms that encode data more efficiently. This transformation reduces storage space requirements while maintaining data integrity through lossless compression techniques that preserve all essential information.
Solution Approach 2:
The system creates compressed copies of the original data that retain all essential information. These compressed representations serve as efficient storage versions while the ability to reconstruct or access original data maintains data integrity when needed.
3Adaptability or versatility
If data from different third-party sources is stored in various formats, then source-specific characteristics are preserved, but data standardization and analysis efficiency are hindered
Solution Approach 1:
The system implements a universal standardized data format that can represent information from multiple third-party sources. This standardized format maintains the essential characteristics of each source while enabling consistent processing and analysis across all data types, thereby improving analysis efficiency without losing source-specific information.
Solution Approach 2:
The system introduces a data standardization layer that acts as an intermediary between diverse third-party data sources and the analysis engine. This intermediary transforms various source formats into a unified standard, preserving source characteristics through metadata while enabling efficient analysis through consistent data structures.
4Loss of information
If all retrieved data is retained without filtering, then comprehensive information is available, but processing time and computational resources increase
Solution Approach 1:
The system extracts and retains only the most relevant and non-redundant data elements from retrieved information. This selective extraction maintains information comprehensiveness for analysis by preserving essential insights while removing duplicate and irrelevant data, thereby reducing processing time and computational resource requirements.
Data Source
AI summary
Systems and methods query third-party data base(s) to obtain object data for each third-party object that is associated with action-based outcomes obtainable via use of the third-party objects, where the data base(s) are queried during a time having relatively less operational strain on system resources. Object data is received from the third-party database(s) and formatting thereof is standardized such that the object data is transformed into a uniform datatype format for analysis. The object data is compressed, which includes encoding the object data to reduce storage size by determining whether stored action-based outcomes are duplicative with updated action-based outcomes identified by the object data. The object data is classified by metadata labeling that includes indicating third-party coding associated with the action-based outcomes and according to indexing rule(s) to facilitate generation of a prioritization list that includes derived improvement(s) available to a user during use of a third-party object.