Cloud Data Uploader Bypasses Security Audits for Faster Transfer
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Solution Overview
Problem
Existing laboratory analytical instruments face challenges in efficiently transferring and visualizing large amounts of data across multiple data sets due to slow data transfer processes and the need to navigate complex data ecosystems with security and auditing protocols, which hinder the aggregation and analysis of data in centralized cloud storage.
Innovation Solution
A computer-implemented method that uses a uploader with a built-in library structure to deserialize data, bypassing the data ecosystem's security and auditing processes, allowing for direct transfer of data to cloud-based storage services like AWS S3, and enables automatic data uploading and visualization across multiple data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is transferred through the data ecosystem interface, then data can be securely stored and accessed, but the transfer process becomes slow due to security systems and auditing protocols
Solution Approach 1:
The patent extracts the data transfer function from the data ecosystem interface by creating a dedicated uploader component that directly communicates with cloud storage. This separates the transfer operation from the security/auditing layers, allowing data to be moved without invoking the full data ecosystem interface stack, thereby significantly improving transfer speed while maintaining security through direct authentication mechanisms.
Solution Approach 2:
The uploader acts as an intermediary component between the data ecosystem and cloud storage. It includes built-in library structures that can deserialize data directly without requiring interface calls to the data ecosystem, serving as a mediator that bypasses the slow interface layer while maintaining secure communication with cloud services.
2Ease of operation
If the data ecosystem interface is used for data transfer, then data can be accessed through centralized storage, but multiple processes run unnecessarily slowing down the transfer
Solution Approach 1:
The patent extracts the essential data transfer capability from the data ecosystem interface by implementing a standalone uploader with embedded library structures. This eliminates the need to invoke multiple data ecosystem processes for each transfer operation, directly improving productivity while maintaining centralized storage access through cloud-based services.
Solution Approach 2:
The system segments the data transfer function into a dedicated uploader component that operates independently from the data ecosystem interface. This segmentation allows the transfer process to run in isolation without triggering unnecessary security and auditing processes, thereby improving transfer efficiency.
3Device complexity
If single-threaded process is used in data ecosystem, then system complexity is reduced, but data uploading competes for resources and reduces performance
Solution Approach 1:
The patent introduces dynamic multi-threading capability in the uploader component, allowing data transfer operations to execute concurrently without requiring changes to the core data ecosystem architecture. The uploader can spawn threads to handle deserialization and cloud communication independently, improving upload performance while maintaining overall system simplicity.
4Ease of operation
If data is transferred manually each time new data is acquired, then data can be stored in centralized location, but the process is repetitive and time-consuming
Solution Approach 1:
The patent implements preliminary configuration of the uploader with built-in library structures and authentication credentials, so that when data needs to be transferred, the system is already prepared and can immediately begin the transfer process without manual setup. This reduces the time required for each transfer operation.
Solution Approach 2:
The uploader component is designed to autonomously handle data transfer operations by including its own library structures for deserialization and cloud communication. It can independently connect to cloud storage services without requiring manual intervention or repeated configuration, enabling self-service data transfer that reduces time loss.
Data Source
AI summary
Exemplary embodiments provide computer-implemented methods, mediums, and apparatuses configured to visualize data stored in a cloud-based storage service. A database in the data storage ecosystem may store results sets from an analytical chemistry system. An uploader may automatically upload the data into cloud storage. Because a great deal of data can be made available this way, more complex analyses can be performed based on visualizations for the data. Examples of analyses performed in connection with these visualizations include product analyses for potency and stability, analyst performance analyses, analytical method analyses, site performance analyses, etc.