Secure Data Transfer System for Scientific Variable Exchange
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Solution Overview
Problem
Conventional systems fail to securely and efficiently transfer data from disparate sources for scientific and medical applications, requiring excessive network bandwidth and memory, and often result in inaccurate or inefficient data processing due to missing variable values.
Innovation Solution
A system and method for securely accessing and transferring data by identifying user-specified variables, performing optimization to reduce memory utilization, and encoding data for statistical analysis, while handling missing values to maintain data integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is transferred from disparate sources using conventional systems, then data exchange functionality is achieved, but network bandwidth and memory utilization become excessive
Solution Approach 1:
The patent extracts and transfers only the specific variables needed by the data consumer from the source system, rather than transferring entire datasets. This selective extraction of required data elements reduces network bandwidth and memory utilization while maintaining data exchange functionality.
Solution Approach 2:
The patent segments the data transfer process into distinct phases: identifying required variables, transferring only those variables, and handling missing values separately. This segmentation allows efficient resource utilization by processing and transferring only necessary data portions.
2Reliability
If data is transferred securely with encryption and validation, then data security and integrity are improved, but processing time and system complexity increase
Solution Approach 1:
The patent performs preliminary identification of required variables and validates data completeness before initiating the transfer process. Missing values are identified and handled in advance, which simplifies the overall system complexity by preventing errors during data transfer and processing.
Solution Approach 2:
The patent introduces an intermediary data transfer system that manages encryption, validation, and coordinate transformation between disparate systems. This intermediary layer handles security and integrity requirements, shielding the underlying complexity from both source and destination systems.
3Adaptability or versatility
If data from disparate sources is transferred without optimization, then data availability is achieved, but memory utilization becomes excessive
Solution Approach 1:
The patent changes the parameters of transferred data by optimizing variable formats and data structures during the transfer process. This parameter optimization reduces memory utilization while maintaining data compatibility and adaptability across disparate systems through coordinate transformation.
4Productivity
If missing variable values are not handled, then data transfer speed is maintained, but data accuracy and statistical analysis quality deteriorate
Solution Approach 1:
The patent performs preliminary identification and handling of missing variable values before data transfer. By detecting and addressing missing data in advance, the system maintains data accuracy for statistical analysis without significantly impacting transfer speed, as the missing value handling is integrated into the initial data preparation phase.
Data Source
AI summary
Systems and methods are disclosed comprising a data transfer and search facility adapted to access content, such as variables and cases, stored on a plurality of disparate computer content storage facilities. The variable data may comprise scientific data. The access of variables may be performed over an encrypted network. A user filter specification may be received and corresponding variables or cases may be identified using a search engine, the user may select which of the identified variables are to be transferred, and the variable data may be transferred from a source storage facility to a destination file. Missing variables values may be identified and a determination is made whether the identified number of missing variables values exceeds a threshold. Encoding of variable data is performed. The variable data may be provided to one or more statistical data analysis and processing applications.