Real-Time Analytics Buffering Optimization Oracle
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Solution Overview
Problem
Existing real-time analytics methods for data streams face computational complexity and high memory requirements, leading to a trade-off between analysis quality and timeliness, as they often need to sacrifice quality to compute results quickly and handle large data structures.
Innovation Solution
A method that accumulates real-time data changes in a buffer unit, generates an optimization problem based on detected events, and uses an optimization oracle like a quantum annealer or coherent Ising machine to perform real-time analytics on a smaller data structure, focusing on differences between successive data points to maintain analysis quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If conventional real-time analytics methods are used to process data streams, then computation speed is improved, but analysis quality deteriorates due to computational complexity
Solution Approach 1:
The patent extracts and processes only the essential features and differences between successive data points rather than analyzing the entire data structure. This selective extraction maintains analysis quality while reducing computational complexity, allowing real-time processing without sacrificing precision.
Solution Approach 2:
The patent segments the data stream into smaller manageable portions and processes them incrementally. By dividing the analysis into discrete steps focusing on local changes rather than global computation, the system achieves both real-time speed and maintained analysis quality.
2Quantity of substance
If conventional real-time analytics methods are used to handle large data structures, then data coverage is improved, but memory requirements worsen
Solution Approach 1:
The patent extracts only the necessary differential information from the data stream rather than storing and processing complete data structures. This approach maintains comprehensive data coverage through incremental analysis while significantly reducing memory requirements by avoiding redundant storage of entire data sets.
3Measurement precision
If graph analysis is performed on streaming data to improve relationship insights, then analytical depth is improved, but computational complexity worsens
Solution Approach 1:
The patent implements a dynamic analysis approach that adapts to changing data patterns in real-time. By continuously updating the analysis based on incoming data differences rather than performing static comprehensive graph analysis, the system maintains deep analytical insights while reducing computational complexity through incremental updates.
Data Source
AI summary
A method and system are disclosed for performing real-time analytics on a plurality of data streams, the method comprising obtaining a plurality of data streams; accumulating real-time changes of the obtained plurality of data streams in a buffer unit to provide a buffered data portion; monitoring the buffered data portion for determining a calculation event, wherein the calculation event is based on a strategy based on observing given features in the buffered data portion; upon detection of the calculation event, generating an optimization problem indicative of the real-time analytics to be performed on one of given data portions of the plurality of data streams and a data structure generated using given data portions of the plurality of data streams; transforming the generated optimization problem into an optimization problem suitable for an optimization oracle; providing the transformed generated optimization problem to the optimization oracle; obtaining at least one solution from the optimization oracle; translating the at least one solution and providing the translated at least one solution to thereby provide the real-time analytics on the plurality of data streams.