Batched Data Reporting Tool for Network Upload Efficiency
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Solution Overview
Problem
Existing systems face challenges in efficiently processing and uploading large datasets across network nodes, leading to increased processing and memory usage, errors, and failures, as they lack the ability to gather comprehensive analytics for entire data processing and uploading jobs.
Innovation Solution
A system comprising an upload tool that breaks data into batches for parallel processing, an analytics tool that determines network node performance and identifies errors, and a reporting tool that generates a single report for multiple users, reducing processing power and memory usage by ensuring data accuracy and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data is processed and uploaded sequentially across network nodes, then data accuracy can be maintained, but data upload speed decreases
Solution Approach 1:
The patent divides the data processing job into multiple independent batches that can be processed in parallel across different network nodes. Each batch is a self-contained unit with its own error handling, allowing simultaneous processing while maintaining overall job integrity through centralized coordination and analytics.
2Reliability
If comprehensive analytics are gathered for entire data processing jobs, then errors and failures can be identified, but processing and memory usage increase
Solution Approach 1:
The patent implements selective analytics that focus only on critical error conditions and key performance metrics rather than comprehensive analysis of all data. The analytics engine prioritizes detecting failures and errors while using summarized statistics instead of full data inspection, reducing processing and memory requirements.
3Measurement precision
If separate data reports are generated for each user, then user-specific data accuracy is ensured, but processing and memory usage increase
Solution Approach 1:
The patent generates a single consolidated data report that contains all necessary information for multiple users. The report structure allows different user groups to extract their specific data needs from the same processed dataset, eliminating redundant processing and memory usage associated with generating separate reports for each user.
4Productivity
If parallel processing is implemented for data batches, then data upload speed improves, but device complexity increases
Solution Approach 1:
The patent introduces a centralized analytics engine and coordination mechanism that acts as an intermediary between parallel processing batches and the final reporting system. This intermediary coordinates error handling, aggregates results from multiple batches, and manages resource allocation, simplifying the overall system architecture while enabling parallel processing.
Data Source
AI summary
A reporting tool includes a retrieval engine, a context switching engine, a reporting engine, a publication engine, and a subscription engine. The retrieval engine retrieves a request for subscription data. The context switching engine receives security information indicating whether a user is authorized to view reporting data. The reporting engine generates a plurality of batches of reporting data. The publication engine generates the data report by processing the batches. The subscription engine communicates the data report.