Data Conversion System for Sparse Electronic Data Projections
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Solution Overview
Problem
Existing systems face challenges in generating accurate data projections for sparse electronic data sets, as they often require large amounts of historical data, leading to inaccurate and unreliable projections when sufficient data is lacking.
Innovation Solution
A data conversion and distribution system comprising a data collection system, a data performance system, and a data distribution system that aggregates and classifies data from multiple sources, generates performance metrics, and predicts performance for sparse data sets by comparing them to aggregated data metrics, enabling accurate projections even with limited data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional projection methods are used with sparse electronic data, then the system can generate projections without requiring large amounts of historical data, but the accuracy and reliability of the projections deteriorate
Solution Approach 1:
The patent combines multiple data sources and aggregators into a unified data collection system. By merging data from various electronic data sources through multiple aggregators, the system accumulates sufficient data volume to enable accurate projections even when individual data sources are sparse. This resolving the contradiction by combining fragmented data into a comprehensive dataset.
Solution Approach 2:
The patent introduces a new dimension of data aggregation by collecting data from multiple sources and time periods, transforming the analysis from a single-data-source perspective to a multi-dimensional aggregated view. This dimensional expansion provides sufficient statistical basis for accurate projections without requiring long historical periods from single sources.
2Speed
If data is collected and processed from multiple sources in real-time, then the system can provide timely data projections, but the processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary data aggregation and classification by organizing data from multiple sources into structured formats with defined schemas and taxonomies before analysis. This pre-processing work is performed continuously in the background, so when projections are needed, the data is already organized and ready for rapid analysis, reducing actual processing time.
Solution Approach 2:
The system creates standardized data copies and representations from multiple sources, transforming diverse data formats into uniform structures. These copied and standardized data representations can be rapidly processed without repeatedly accessing and parsing original diverse sources, significantly reducing processing time while maintaining data integrity.
3Reliability
If electronic data from multiple sources is aggregated and classified, then the system can generate comprehensive performance metrics, but the system complexity and infrastructure requirements increase
Solution Approach 1:
The patent divides the complex data aggregation and processing system into multiple independent aggregators, each responsible for specific data sources or data types. This segmentation allows each component to be managed, maintained, and scaled independently, reducing overall system complexity while maintaining comprehensive data collection capabilities through the coordinated work of multiple specialized aggregators.
Solution Approach 2:
The patent implements universal data schemas, taxonomies, and processing frameworks that can handle multiple data sources and types through a single standardized interface. This multi-functional approach allows the system to process diverse data from various sources using the same infrastructure components, reducing complexity compared to having separate specialized systems for each data source.
Data Source
AI summary
Systems and methods for data conversion and distribution. A data collection system receives data from plural data sources; extracts data values for each data source from the data; classifies each extracted value among categories; and stores each extracted value in a database according to a data mapping based on a category index, such that the extracted value is stored according to the associated category index. A data performance system retrieves, for each category, data values associated with the category and a predetermined period, by querying the database based on the category index; and runs, for each category, the retrieved data through statistical algorithms, to generate data performance metrics representative of the data from all of the data sources. A data distribution system generates a predictive performance metric for further data based on a comparison between the further data and the data performance metrics for at least one category.