Data Stream Classification via Atomic Point Clustering
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Solution Overview
Problem
Conventional data analysis systems face challenges in managing low-quality data, scalability with increasing data volumes, and accurately classifying disparate data sources, leading to incorrect conclusions and difficulty in distinguishing noteworthy trends from artificial anomalies.
Innovation Solution
The system implements a method to classify unstructured data streams by mapping metadata to stored formats, recursively parsing data components to retrieve atomic level points, and clustering them based on common attributes, allowing for source-agnostic data processing and storage, thereby isolating intrinsic properties and maintaining data consistency across varying formats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data analysis systems process large volumes of data from multiple sources, then data processing capability is improved, but data quality deteriorates due to format variability and inconsistency
Solution Approach 1:
The patent introduces a classification system as an intermediary layer between data sources and analysis systems. This system automatically categorizes incoming data streams based on their format and characteristics, routing them to appropriate processing pipelines. The classifier acts as a mediator that handles format variability without requiring the core analysis system to adapt to each source's specific format, thus maintaining data quality while processing large volumes from multiple sources.
Solution Approach 2:
The patent segments the data processing system into distinct classification pipelines, each handling specific data formats or source types. By dividing the processing workload into separate categorized streams, the system can maintain high processing capacity for large data volumes while ensuring each segment receives consistent, quality-controlled data processing, preventing quality deterioration from format variability.
2Adaptability or versatility
If data classification is performed on disparate data sources, then data organization is improved, but system complexity increases due to format variability
Solution Approach 1:
The patent implements a universal classification system that can handle multiple data formats and sources through a single unified architecture. The classifier is designed with multi-functionality to recognize and categorize various data types without requiring separate processing logic for each format. This universal approach improves data organization across disparate sources while avoiding the complexity of implementing format-specific processing systems for each data type.
3Productivity
If hardware resources are scaled up to process more data, then data processing capacity is improved, but performance per resource deteriorates due to configuration constraints
Solution Approach 1:
The patent implements dynamic resource allocation where the classification system can adaptively route data to available processing resources based on current system state and data characteristics. This dynamic approach allows the system to scale processing capacity by utilizing multiple resources simultaneously while maintaining optimal performance per resource through adaptive load distribution, avoiding the performance degradation that would result from static configurations in scaled-up systems.
Data Source
AI summary
Disclosed herein are embodiments of systems, methods, and apparatus that execute classification techniques to enable high-quality analysis of ingest data by interpreting and categorizing disparate data points of the ingest data. The execution of the classification techniques leads to isolation of intrinsic properties of each data point to represent the essence of what the overall ingest data indicates. The classification techniques further enables classification of the ingest data, which is unencumbered by any ingest data format changes, such as ordering of data components, encoding, or properties associated with the ingest data that are likely to change without altering meaning conveyed by the ingest data.