AI Data Collectors with Pre-Queue Filtering
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Solution Overview
Problem
The collection and preparation of datasets for machine learning models are time-consuming and expensive, often requiring large amounts of input data and manual processes, with existing data streaming applications lacking an intelligence layer to filter relevant data effectively.
Innovation Solution
The development of automated data collectors with pre-queue and post-queue workflows that filter and process input data streams, allowing only relevant data to be passed to output sinks, reducing the need for manual intervention and additional coding, and enabling the use of multiple data sources for machine learning applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated collectors with pre-queue and post-queue workflows are implemented, then data collection efficiency and relevance are improved, but system complexity increases
Solution Approach 1:
The data collection system is divided into distinct functional modules: pre-queue workflow for initial filtering, queue for buffered storage, and post-queue workflow for final processing. This segmentation allows each component to be optimized independently while maintaining overall system productivity through parallel processing capabilities.
Solution Approach 2:
The pre-queue workflow performs preliminary filtering and validation of incoming data objects before they enter the main queue. This preliminary action reduces the burden on subsequent processing stages by eliminating obviously irrelevant data early in the pipeline, thereby improving overall efficiency without requiring complete system redesign.
2Reliability
If manual data collection and preparation processes are used, then data relevance can be ensured through human judgment, but time consumption and costs increase
Solution Approach 1:
The automated collector performs self-service by autonomously executing pre-queue and post-queue workflows to filter and process data objects. The system uses configurable criteria and machine learning models to evaluate data relevance automatically, eliminating the need for manual human judgment while maintaining high standards of data quality through iterative optimization.
Solution Approach 2:
The system implements feedback mechanisms where post-queue workflow results are analyzed to refine pre-queue filtering criteria over time. This continuous feedback loop allows the automated system to improve its data selection accuracy, approaching human-level judgment quality while maintaining automated speed and efficiency.
3Reliability
If large amounts of input data are collected for model training, then model performance can be improved, but data collection costs and complexity increase
Solution Approach 1:
The system applies partial action by collecting and processing only the subset of data objects that meet predefined relevance criteria through pre-queue and post-queue workflows. Rather than collecting all available data, the system selectively processes relevant portions, reducing collection complexity while maintaining sufficient data volume and quality for effective model training and performance improvement.
Data Source
AI summary
Systems, methods and computer program code are provided to process an input data object from a data source, including identifying at least a first collector associated with the data source, adding the input data object to a queue of the at least first collector, and applying a post-queue workflow to the input data object to determine whether to pass the input data object from the queue to an output data sink.