Real-Time Transfer Failure Detection in Bulk Data Processing
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Solution Overview
Problem
Existing data transfer systems face delays in detecting failures in batch data transfer configuration files, leading to potential missed payment dates and processing inefficiencies due to delayed notifications.
Innovation Solution
A computer system with a communications module and processor that receives a bulk transfer file, classifies transfers likely to fail using a trained classifier, and provides real-time or near-real-time notifications, allowing for immediate corrections and filtering of successful transfers for batch processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If batch data transfer configuration files are processed using traditional batch processing, then processing capacity is improved, but detection time for transfer failures increases
Solution Approach 1:
The system performs preliminary validation of transfer configuration files before batch processing begins. By detecting and reporting potential transfer failures in advance (during file upload and classification), the system enables corrective actions to be taken before the batch processing executes, thus resolving the contradiction between high processing capacity and delayed failure detection
Solution Approach 2:
The system implements real-time feedback mechanisms during the batch processing workflow. When transfer failures are detected through the classifier, the system immediately notifies users and provides feedback about the status of individual transfers within the batch, allowing for dynamic adjustments and preventing delayed detection of failures
2Speed
If real-time classification and notification is implemented, then transfer failure detection speed is improved, but system complexity increases
Solution Approach 1:
The system introduces a classification module as an intermediary component between the data transfer configuration file and the batch processing system. This intermediary uses machine learning models to predict transfer failures and generates notifications, thereby achieving real-time detection speed without requiring complete system redesign - the classification layer acts as a manageable intermediate layer
3Loss of time
If all transfers are processed immediately, then processing time is reduced, but resource consumption increases
Solution Approach 1:
The system applies local quality by selectively processing transfers based on their individual risk profiles. Through the classification module, transfers are differentiated into high-risk (likely to fail) and low-risk categories. The system then applies different handling strategies to each category - immediate notification and selective processing for high-risk transfers, while maintaining efficient batch processing for low-risk transfers, thus reducing overall resource consumption while maintaining fast processing time
Data Source
AI summary
Methods and computer systems for processing bulk transfer files are described. In an aspect, a transfer processing system is configured for: receiving a bulk transfer file from a remote device, the bulk transfer file defining a plurality of requested transfers associated with a database; classifying one or more of the requested transfers defined in the bulk transfer file as being likely to fail processing by passing the requested transfers defined in the bulk transfer file to a classifier trained to identify transfers likely to fail processing based on training data, the training data including a plurality of prior requested transfers and associated completion indicators indicating the prior requested transfers that failed processing; and providing, in real time or near real time, a notification to the remote device of the requested transfers identified as likely to fail processing.