Data Pull Scheduling Using Predicted Availability and Rate Limits
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Solution Overview
Problem
Data aggregators waste processing, power, and network resources by performing data pulls that yield no new information due to outdated data records and exceeding rate limits imposed by data hosts.
Innovation Solution
Implementing models to predict data availability and rank data pulls based on likelihood of new information, allowing rescheduling or skipping pulls to conserve resources and adhering to rate limits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data pulls are performed periodically to ensure data freshness, then data availability is improved, but processing resources and network bandwidth are wasted when no new information is available
Solution Approach 1:
The system performs preliminary actions by predicting future data availability using machine learning models before actually pulling data. The model analyzes historical patterns, data update frequencies, and time-based characteristics to forecast when new information is likely to become available, allowing the system to schedule pulls proactively rather than reactively.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual data update patterns and comparing them against model predictions. This feedback loop allows the machine learning model to be retrained and refined based on real-world performance, improving prediction accuracy over time and enabling more efficient pull scheduling.
2Reliability
If data pulls are performed frequently to capture new information, then data freshness is improved, but network bandwidth and processing power are consumed unnecessarily
Solution Approach 1:
The system uses machine learning models to perform preliminary prediction of data availability, analyzing historical pull patterns, data update frequencies, and temporal characteristics to forecast optimal pull timing before actual data retrieval occurs.
Solution Approach 2:
The system dynamically adjusts pull scheduling based on real-time conditions and predicted data availability patterns. The machine learning model continuously adapts to changing data update behaviors, allowing the system to optimize pull frequency and timing dynamically rather than using fixed periodic schedules.
3Ease of operation
If data pulls are scheduled based on fixed periodicity, then implementation simplicity is maintained, but rate limits imposed by data hosts are exceeded
Solution Approach 1:
The system replaces fixed periodic scheduling with dynamic, adaptive scheduling that adjusts pull timing based on predicted data availability patterns and host rate limit constraints. The machine learning model analyzes historical data to determine optimal pull intervals that respect rate limits while maintaining data freshness.
Solution Approach 2:
The system changes scheduling parameters dynamically based on predicted data update patterns and rate limit constraints. Instead of using fixed time intervals, the system adjusts pull timing and frequency parameters according to actual data availability characteristics and host-imposed rate limits.
4Quantity of substance
If all scheduled pulls are executed regardless of predictions, then data retrieval completeness is maintained, but processing resources are wasted on pulls yielding no new information
Solution Approach 1:
The system extracts and filters out scheduled pulls that are predicted to yield no new information based on machine learning predictions. By identifying and excluding these futile pulls from execution, the system maintains data retrieval completeness for meaningful updates while eliminating wasted resources on redundant pulls.
Solution Approach 2:
The system applies partial action by selectively executing only those data pulls that are predicted to return new information, rather than executing all scheduled pulls. This partial execution approach maintains sufficient data freshness while significantly reducing resource consumption on pulls that would yield no valuable data.
Data Source
AI summary
In some implementations, a data aggregator may receive an indication associated with a data record. The data aggregator may apply a model to the indication to generate a prediction regarding when new information associated with the data record will be available. Based on the prediction, the data aggregator may refrain from requesting new information and may schedule a pull for new information associated with the data record for a later time. Additionally, or alternatively, the data aggregator may receive an indication associated with a plurality of data pulls that are associated with a plurality of data records and may receive an indication of a rate limit associated with a host for the plurality of data records. The data aggregator may apply rules to generate a ranking of the plurality of data pulls and may schedule the plurality of data pulls based on the ranking and the rate limit.