Probabilistic Storage Elements for Data Stream Ratio Tracking
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Solution Overview
Problem
Traditional methods for creating real-time summaries of data streams are complex and require circuitry for division operations, making it difficult to differentiate the impact of various events in real-time.
Innovation Solution
The use of fixed maximum length probabilistic storage elements, such as estimator modules with random number generators and storage modules, to simplify the tracking of ratios in data streams and allow for differential treatment of events without requiring dividers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods with division circuitry are used to create real-time summaries of data streams, then measurement precision can be maintained, but device complexity increases significantly
Solution Approach 1:
The patent replaces mechanical division circuitry with a probabilistic storage element system that uses random number generators and storage modules to approximate ratio calculations. This substitution eliminates the need for complex division hardware while maintaining functional capability through statistical sampling and probabilistic representation of data stream ratios.
Solution Approach 2:
The patent creates a simplified copy of the ratio tracking function using probabilistic storage elements instead of exact division circuitry. The estimator modules generate random samples that statistically represent the ratio, providing a functional copy that is less complex but still accurate enough for real-time summary applications.
2Measurement precision
If traditional ratio tracking methods are used, then accurate real-time summaries can be generated, but the ability to differentiate impact of various events is reduced
Solution Approach 1:
The patent applies local quality by allowing different estimator modules to treat events with different impacts through their individual random number generation and storage mechanisms. Each module can independently process events with varying weights or priorities, enabling the system to differentiate between important and less important events while maintaining overall ratio accuracy.
Solution Approach 2:
The patent introduces dynamics by using probabilistic storage elements that can adaptively update their states based on incoming events. The random number generators create dynamic sampling processes that allow the system to respond differently to various event types, enabling real-time adaptation to different impact levels without requiring complex predetermined weighting circuitry.
3Device complexity
If simplified storage elements without division circuitry are used, then device complexity is reduced, but the traditional method of creating real-time summaries becomes more difficult
Solution Approach 1:
The patent implements self-service by designing the probabilistic storage elements to automatically maintain ratio representations through their internal random number generation and storage mechanisms. The estimator modules self-update their states based on incoming data stream events, eliminating the need for external division circuitry or complex control logic while continuing to produce accurate real-time summaries.
Data Source
AI summary
A ratio of events in a data stream can be probabilistically maintained using a machine which comprises an observation line and circuitry for maintaining the ratio. In such a machine the circuitry may comprise a set of estimator modules, each of which comprises a random number generator and a storage module, wherein the set of estimator modules may comprise a highest order estimator module and a lowest order estimator module.