Dynamic Sample Rate Control for Networked Measurement Systems
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Solution Overview
Problem
Current technologies face challenges in managing data flows from millions of browsers displaying advertisements, as reducing sample rates to alleviate data overload compromises statistical accuracy, making it difficult to maintain system integrity.
Innovation Solution
A system and method that dynamically control sample rates and data flow by determining statistical significance, allowing for real-time sampling and autonomous decision-making on data transmission, using a script on each browser to randomize sampling and adjust rates based on capacity constraints, ensuring statistical accuracy without overwhelming servers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If sample rates are reduced to alleviate data overload, then data flow to servers is reduced, but statistical accuracy is compromised
Solution Approach 1:
The patent implements dynamic sample rate adjustment where the sampling rate is not fixed but changes over time based on system conditions. The controller monitors data flow volume and statistical accuracy metrics, then dynamically modifies the sample rate to optimize both data flow reduction and statistical precision maintenance. This dynamic approach allows the system to adapt to varying traffic conditions while preserving measurement integrity.
Solution Approach 2:
The system changes the sampling parameter (sample rate) based on monitored conditions. When data flow exceeds thresholds or statistical accuracy degrades, the sample rate parameter is adjusted. This parameter change mechanism enables the system to balance data flow management with statistical accuracy requirements by modifying the sampling frequency in response to system state.
2Measurement precision
If sample rates are increased to maintain statistical accuracy, then statistical significance is improved, but data overload increases
Solution Approach 1:
The patent employs feedback mechanisms where the controller continuously monitors both data flow volume and statistical accuracy metrics. Based on this feedback, the system adjusts the sample rate to maintain statistical significance while preventing data overload. The feedback loop ensures that statistical accuracy requirements drive sample rate decisions while data flow constraints are respected.
Solution Approach 2:
Rather than using a fixed high sample rate, the system dynamically adjusts sampling intensity based on real-time conditions. When statistical accuracy metrics indicate sufficient data quality, the sample rate is reduced to prevent overload. This dynamic modulation allows the system to maintain statistical significance only when necessary, reducing overall data flow volume.
3Adaptability or versatility
If data is collected from millions of browsers simultaneously, then comprehensive coverage is achieved, but system capacity is exceeded
Solution Approach 1:
The patent segments the data collection process by implementing hierarchical sampling control. Instead of uniformly sampling from all millions of browsers, the system divides the population into segments and applies different sample rates to different segments based on their contribution to statistical accuracy and system load. This segmentation allows comprehensive coverage through strategic sampling rather than exhaustive data collection.
Solution Approach 2:
The system applies local quality control by adjusting sample rates at individual browser or segment levels rather than using a single global rate. High-priority segments or browsers contributing critical data receive higher sample rates, while less critical segments receive lower rates. This localized approach maintains system coverage for important data sources while reducing overall processing capacity requirements.
Data Source
AI summary
A system and methods for dynamically controlling sample rates and data flow in a distributed networked environment by dynamic determination of statistical significance or characteristics for an unlimited number of data collection scripts concurrently executed on concurrently rendering web pages operating an unlimited number of advertisements. Consumer and media behaviors are sampled on all the different components of the distributed environment to gather information, which is transmitted to a downstream statistical analytics system. The system and methods are configured to balance the communication data flow and load among servers and browsers in this distributed networked environment that are engaged in viewing of online content including online content with one or more advertisements.