Multi-Tiered Data Store Architecture for Secure Storage Networks
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Solution Overview
Problem
Existing data storage systems in secure networks face challenges in isolating data access to prevent dirty data read scenarios and in efficiently managing and prioritizing data access based on temporal data.
Innovation Solution
The solution involves a method that communicatively couples an object database with multiple applications to form a secure storage network. This network activates primary, secondary, and tertiary data stores to manage index data, allowing for selective data access and prioritization based on temporal data, thereby preventing dirty reads and optimizing data access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single data store is used to store index data, then device complexity is reduced, but data access isolation and dirty read prevention capability deteriorate
Solution Approach 1:
The patent divides the data storage system into multiple independent data stores (first data store, second data store, third data store) with different sizes and access characteristics. Each data store handles specific access patterns, enabling isolation of data access operations and prevention of dirty reads while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent introduces a data access controller that acts as an intermediary between data requests and the multiple data stores. This controller manages the complexity by selecting appropriate data stores based on access patterns and prioritization rules, while presenting a simplified interface for data access operations.
2Productivity
If data access is prioritized based on temporal data, then data access efficiency is improved, but data access control complexity deteriorates
Solution Approach 1:
The patent implements dynamic data access prioritization where the system adjusts access patterns based on temporal data and operational context. The data access controller dynamically selects which data stores to query based on real-time conditions, enabling efficient prioritized access without requiring complex static control mechanisms.
Solution Approach 2:
The system automatically manages data access prioritization by monitoring temporal patterns and operational needs. The data access controller self-adjusts access strategies based on observed data access patterns, eliminating the need for manual configuration of complex access control rules while maintaining high productivity.
3Reliability
If multiple data stores of different sizes are activated, then data access isolation and prioritization capability are improved, but device complexity and resource consumption deteriorate
Solution Approach 1:
The patent creates data stores with different local qualities - the first data store has a larger size for comprehensive indexing, while the second and third data stores have smaller sizes optimized for specific access patterns. Each data store is tailored to handle specific operational requirements, enabling effective data access control without requiring all stores to have maximum capacity.
Solution Approach 2:
The system activates multiple data stores with varying sizes based on actual operational needs rather than always using maximum capacity. The data access controller selectively queries appropriate data stores for specific operations, avoiding the resource consumption of maintaining all stores at full capacity while still providing comprehensive data access control when needed.
Data Source
AI summary
The disclosed method includes: communicatively coupling an object database, a first application, and a second application; loading: a first set of data indices into a first primary data store, a second set of data indices into a first secondary data store, a third set of data indices into a first tertiary data store, and the third set of data indices into a second tertiary data store; modifying: at least one first data element comprised in the third set of data indices, and at least one second data element; initiating a data deduplication computing operation by selectively marking data elements within the first secondary data store that map to the one of the first modified data element and the second modified data element; and merging the one of the first modified data element and the second modified data element with data elements comprised in the first secondary data store.