Data Stream Classification via Matrix Buffer Segmentation
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Solution Overview
Problem
Current data processing systems face challenges in efficiently classifying and filtering large data streams from IoT devices and social media, leading to excessive storage needs, inefficiencies in real-time analysis, and scalability issues due to the von Neumann bottleneck, where only a small subset of data is relevant, and real-time synchronization with multiple sensors is difficult.
Innovation Solution
A computer-implemented method and system that receives data from incoming streams, separates and processes it using a selection configuration matrix, and utilizes a neural network component for real-time classification and filtering, optimizing storage by discarding irrelevant data and using a hierarchical storage system, while overcoming the von Neumann bottleneck through parallel processing units and non-von Neumann neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is stored in traditional sequential processing systems, then data storage capacity is increased, but processing speed and real-time analysis capability deteriorate due to the von Neumann bottleneck
Solution Approach 1:
The system segments data streams into multiple parallel processing channels, each handled by independent processing units. This allows simultaneous processing of different data portions, bypassing the sequential bottleneck of traditional von Neumann architecture and enabling real-time analysis of large data volumes.
Solution Approach 2:
The patent transitions from sequential single-dimensional processing to parallel multi-dimensional processing by implementing multiple processing units that operate simultaneously on different data streams. This dimensional shift from serial to parallel architecture resolves the speed-storage contradiction.
2Loss of information
If all data from data streams is stored and processed, then data completeness is improved, but storage requirements and processing complexity worsen
Solution Approach 1:
The system extracts only the most relevant features and data points from incoming streams using filtering mechanisms and selection configuration matrices. By taking out only essential information for classification, the system maintains data completeness for critical parameters while significantly reducing overall storage requirements.
Solution Approach 2:
Different data streams and data points receive different levels of processing and storage priority based on their relevance. The system applies local quality control by storing high-priority data with full detail while using compressed or aggregated representations for lower-priority data, optimizing storage efficiency.
3Measurement precision
If multiple sensors are synchronized in real-time, then data synchronization accuracy is improved, but system complexity and processing overhead worsen
Solution Approach 1:
The patent merges multiple sensor inputs into a unified processing framework using a selection configuration matrix that coordinates data from multiple sources. This consolidation approach maintains synchronization accuracy while reducing the overall system complexity compared to managing each sensor independently.
Solution Approach 2:
The processing units are designed with universal functionality to handle multiple sensor types and data formats simultaneously. This multi-functional design enables real-time synchronization of diverse sensors without requiring separate processing pipelines for each sensor type, thereby reducing system complexity.
4Adaptability or versatility
If traditional machine learning algorithms are used for real-time classification, then algorithm compatibility is improved, but processing speed and confidence level worsen
Solution Approach 1:
The system replaces traditional sequential machine learning algorithms with parallel processing architectures that can evaluate multiple classification models simultaneously. This substitution maintains compatibility with various algorithms while dramatically improving processing speed and confidence levels through concurrent evaluation.
Data Source
AI summary
Methods, computer program products, devices, and/or systems are provided that perform the following operations: receiving data from an incoming data stream; separating the data into data chunks and storing the data chunks in accordance with a selection configuration matrix in a set of different matrix buffers until the matrix buffers are filled up; processing the data chunks by multiple processing units, each processing unit associated to a matrix buffer of the matrix buffers via an I/O block; providing the processed data chunks as inputs to a neural network component; and classifying the data and filtering the data based on a classification of the data by the neural network component.