Linked Data Processor for Distributed Database Storage
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Solution Overview
Problem
Traditional relational database technologies are not well-suited for distributed computing environments, as they require synchronous behavior and full access to data, leading to high computing costs and reduced reliability in cloud-based services.
Innovation Solution
A linked data model with a tree format is used for storing and retrieving data, where data items are addressable and linked based on business logic, allowing for efficient storage and retrieval by traversing data storage trees, reducing the need for query translation and processor time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional relational database technology is used, then data storage and retrieval can be performed, but computing costs increase and reliability decreases in distributed computing environments
Solution Approach 1:
The patent segments the monolithic relational database into distributed data stores across multiple nodes. Each node maintains independent data partitions with local storage and processing capabilities, eliminating the single-point-failure architecture of traditional databases while reducing centralized computing overhead.
Solution Approach 2:
The system implements self-service through automated data replication, sharding, and fault detection mechanisms. Each distributed node autonomously manages its local data partitions and can independently handle queries without requiring centralized coordination, reducing overall system overhead and improving reliability.
2Productivity
If synchronous behavior and full data access are required, then data consistency can be maintained, but processor time and overhead increase
Solution Approach 1:
The patent implements local quality by allowing each distributed node to maintain data consistency independently through local transactions and validation rules. Instead of requiring global synchronous access, each node enforces data integrity constraints locally, reducing processor overhead while maintaining reliability at the node level.
Solution Approach 2:
The system performs preliminary validation and consistency checks at data insertion points before distribution. Data is pre-validated and structured to maintain consistency requirements upfront, eliminating the need for continuous synchronous verification during query operations, thus reducing processor time while preserving data consistency.
3Loss of time
If traditional database query translation is performed, then complex queries can be executed, but retrieval time and computational overhead increase
Solution Approach 1:
The patent replaces the mechanical query translation process with direct data access patterns. Instead of translating complex queries into multiple joins and transformations, the distributed architecture enables direct retrieval of pre-segmented data partitions, eliminating translation overhead and reducing retrieval time.
Solution Approach 2:
Data is pre-organized and indexed at distribution points according to anticipated query patterns. This preliminary structuring allows direct access to relevant data partitions without requiring complex runtime translation, significantly reducing retrieval time while simplifying the query execution mechanism.
Data Source
AI summary
Systems and methods are provided for storing and retrieving data using a linked data model having a tree format. Instead of using a traditional relational database, a linked data model can be used with links between data items that reflect the underlying business logic. To facilitate retrieval of data, the data items stored using the linked data model can be individually addressable.