Data Conversion Layer for Sparse-Data Projection Accuracy
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Solution Overview
Problem
Existing systems struggle to generate accurate data projections and statistical analyses from sparse electronic data due to the lack of sufficient historical data, leading to unreliable results.
Innovation Solution
A data conversion and distribution system comprising a data subscription unit and a virtual machine that processes data from multiple sources, reformats and aggregates it, and applies statistical algorithms to generate unified data, enabling the creation of accurate data sensitivities and projections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional projections are used based on sparse data, then the system can operate with limited historical data, but the accuracy and reliability of projections deteriorates
Solution Approach 1:
The patent introduces an intermediary data enrichment layer that bridges sparse historical data and projection needs. This layer incorporates external data sources, machine learning models, and statistical algorithms that act as mediators to transform sparse data into reliable projections by filling gaps with inferred information from related data classes and patterns.
Solution Approach 2:
The system changes the parameters of data representation by transforming sparse historical data into enriched feature sets that include derived attributes, temporal patterns, and relationships with related data classes. This parameter transformation enables accurate projections even when original historical data is sparse.
2Reliability
If additional data is collected to improve projection accuracy, then the reliability of projections improves, but the time required to obtain and process data increases
Solution Approach 1:
The system performs preliminary data enrichment and feature engineering in advance, pre-processing historical data to create ready-to-use projection inputs. This preliminary action reduces the time needed during actual projection generation by having prepared data structures and pre-computed features available for immediate use.
Solution Approach 2:
The patent implements continuous data enrichment processes that operate alongside data collection, continuously updating and refining projection inputs. This continuous action eliminates idle time between data collection and projection generation, maintaining useful processing activity throughout the data acquisition period.
3Speed
If data is processed in real-time to improve timeliness, then the speed of projection generation improves, but the complexity of the processing system increases
Solution Approach 1:
The patent segments the projection generation process into distinct modular components: data collection modules, data enrichment modules, statistical analysis modules, and projection generation modules. This segmentation enables real-time processing of individual components while maintaining overall system manageability through modular architecture.
Solution Approach 2:
The system employs universal processing frameworks and multi-functional algorithms that can handle various data types and projection scenarios through a single unified platform. This universality reduces the need for separate specialized systems, thereby reducing overall complexity while maintaining real-time processing capability.
Data Source
AI summary
Systems and methods for improved data conversion and distribution are provided. A data subscription unit is configured to receive data and information from a plurality of data source devices. The data subscription unit is in communication with a virtual machine that includes backtesting utility configured to generate backtesting data using one or more statistical models and one or more non-statistical models. The backtesting utility may translate the backtesting results into one or more interactive visuals, and generate a graphical user interface (GUI) for displaying the backtesting results and the one or more interactive visuals on a user device. The backtesting utility may update one or more of the displayed backtesting results and the one or more interactive visuals without re-running the modeling steps.