Distributed Hierarchical Data Storage and Classification
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Solution Overview
Problem
Current data storage systems face challenges in efficiently managing and classifying large volumes of network session data across multiple servers and storage devices, particularly in distributing and processing hierarchical data structures effectively.
Innovation Solution
The implementation of distributed storage systems using machine-learning algorithms to generate hierarchical data structures on multiple servers, along with a derivative hierarchy that maps nodes across these structures, allowing for efficient data retrieval and classification without altering the underlying data structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If hierarchical data structures are distributed across multiple servers and storage devices, then storage capacity and data volume handling are improved, but system complexity and difficulty of management increase
Solution Approach 1:
The patent segments hierarchical data structures into multiple distributed instances across different servers and storage devices. Each server maintains portions of the hierarchy, allowing the system to scale storage capacity while managing complexity through modular organization of data across distributed nodes.
2Measurement precision
If machine-learning algorithms are used to generate hierarchical data structures, then data classification and pattern recognition are improved, but processing time and computational resources increase
Solution Approach 1:
The patent applies machine-learning algorithms to pre-process and classify network session data before storing it in hierarchical structures. By performing classification operations in advance during data ingestion, the system reduces the need for time-consuming processing during query operations, thereby decreasing overall processing time while maintaining high classification accuracy.
3Measurement precision
If updates are made to underlying hierarchical data structures, then data accuracy and relevance are improved, but system stability and operational continuity are affected
Solution Approach 1:
The patent implements a nested hierarchy where a derivative hierarchy is built upon underlying hierarchical data structures. The derivative hierarchy provides an additional layer of abstraction that shields the underlying structures from direct access. This allows updates to the underlying hierarchies without disrupting operations on the derivative hierarchy, thereby maintaining system stability while improving data accuracy through updates.
4Adaptability or versatility
If multiple different hierarchical data structures are maintained for different users and algorithms, then adaptability and customization are improved, but system complexity and management difficulty increase
Solution Approach 1:
The patent creates a universal derivative hierarchy that can serve multiple users and applications simultaneously. This single hierarchical structure is designed to accommodate different user requirements and machine-learning algorithms through a common interface and unified data organization, thereby providing adaptability and customization without requiring separate management of multiple independent hierarchical structures.
Data Source
AI summary
The present disclosure generally relates to storing, processing, and classification of content resources, such as documents, web-based resources, and other content. More particularly, the present disclosure describes techniques for distributed storage of network session data in hierarchical data structures stored on multiple servers and/or physical storage devices, and techniques for analyzing and classifying the distributed hierarchical structures. Such techniques may include executing different machine-learning algorithms on different servers and/or different storage devices, and generating node mapping data between a plurality of different hierarchical structures and a top-level derivative hierarchy that references the underlying hierarchical structures in order to access and manage the different distributed taxonomies within the underlying hierarchical structures.