Streaming Accumulator for Real-Time Statistical Analysis
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Solution Overview
Problem
Current data processing systems face challenges in analyzing high-speed streaming data with minimal latency, which is crucial for real-time insights in fields like statistical arbitrage and risk assessment.
Innovation Solution
A method and apparatus for analyzing streaming data using a streaming accumulator with addition modules and multiplexers that continuously calculate basic statistical elements and descriptors, enabling real-time computation of statistical characteristics with low latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional data processing systems are used to analyze streaming data, then data processing capability is provided, but latency is high and real-time insights cannot be achieved
Solution Approach 1:
The system segments the data processing pipeline into distinct functional modules: multiplexers for data selection, accumulation units for statistical calculation, and output modules for descriptor generation. This segmentation enables parallel processing of different statistical metrics simultaneously, reducing overall latency while maintaining comprehensive data processing capability.
Solution Approach 2:
The system performs preliminary calculations of basic statistical elements (sums, counts, minima, maxima) as data streams are received, rather than waiting for complete datasets. These pre-computed elements are then combined to generate final statistical descriptors, enabling real-time insights without sacrificing processing thoroughness.
2Loss of time
If real-time statistical calculation is implemented, then latency is reduced, but system complexity increases
Solution Approach 1:
The accumulation units are designed as multi-functional components that simultaneously compute multiple statistical metrics (sums, counts, minima, maxima) from the same data stream. This universal approach reduces the need for separate dedicated circuits for each statistic, thereby lowering overall system complexity while achieving real-time calculation of multiple descriptors.
Solution Approach 2:
The system employs a hierarchical structure where basic statistical elements (simple sums and counts) are nested within more complex descriptor calculations. Higher-level statistical descriptors are constructed by combining these nested basic elements, allowing the system to manage complexity through modular composition rather than monolithic processing.
Data Source
AI summary
A method for analyzing streaming data includes providing a streaming accumulator comprising an addition module and two multiplexers, receiving one or more data streams, continuously calculating a set of basic statistical elements, receiving a request to calculate a set of statistical descriptors, calculating the set of statistical descriptors, and providing the set of statistical descriptors. An apparatus for analyzing streaming data includes a first multiplexer configured to receive a first summation, a second summation, and a current data item, and forward the first summation on cycles 1 and 3, forward the second summation on cycle 4, and forward the current data item on cycle 2, a second multiplexer configured to receiver the second summation, a third summation, and a previous data item, and forward the previous data item on cycles 1 and 3, forward the second summation on cycle 4, and forward the third summation on cycle 2.