ARB Knowledge Database for Redundancy-Free Query Retrieval
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Solution Overview
Problem
Existing databases struggle with efficient storage, retrieval, and manipulation of complex data patterns, leading to redundancy and inefficiencies in processing and querying operations.
Innovation Solution
The ARB paradigm integrates graph and relational databases with query languages and functional programming to enhance data organization, allowing for more flexible retrieval and manipulation, reducing redundant information, and optimizing storage and processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional relational or graph databases are used to store data, then data can be stored in organized structures, but redundant information is generated and processing efficiency decreases
Solution Approach 1:
The patent extracts only the essential relationships between entities using the ARB (Agent-Relation-Object) paradigm. By representing data as triplets of (agent, relation, object), the system removes redundant metadata and intermediate representation layers that traditional databases require, storing only the core semantic relationships needed for retrieval and processing.
Solution Approach 2:
Instead of organizing data around rigid database schemas or pre-defined graph structures, the patent inverts the approach by organizing data around flexible ARB triplets that can represent any relationship type. This allows the system to adapt to different data patterns without requiring structural changes, eliminating the need for redundant schema definitions and index structures.
2Ease of operation
If complex data patterns are stored in traditional databases, then data can be retrieved using standard query languages, but query response times increase and processing becomes inefficient
Solution Approach 1:
The patent changes the fundamental parameters of data representation from traditional database rows and columns or graph nodes and edges to ARB triplets. This parameter change enables more efficient pattern matching and retrieval operations, as the triplet structure directly encodes the semantic relationships needed for querying, eliminating the need for complex join operations and multi-step query processing.
Solution Approach 2:
The ARB triplet structure serves multiple functions simultaneously: it represents entities, relationships, and attributes in a unified format; it enables both storage and retrieval operations; and it supports various query patterns without requiring different data structures. This multi-functionality reduces the overhead of maintaining multiple specialized structures and improves overall processing efficiency.
3Quantity of substance
If more data is stored per unit of space, then storage efficiency improves, but data processing and manipulation become more complex
Solution Approach 1:
The patent segments data into discrete ARB triplets, each representing a complete semantic relationship. This segmentation allows the system to store高密度 information while maintaining simple, modular processing. Each triplet is an independent unit that can be processed individually, reducing the complexity of handling large datasets compared to traditional row-based or graph-based structures that require coordinated processing of multiple interconnected elements.
Data Source
AI summary
An optimal method of storing, computing, and communicating knowledge is disclosed herein. The method of organizing computer information which allows for more flexible retrieval and manipulation using an ARB paradigm. This paradigm combines and extends elements of a graph database, row-column database, query language, declarative logic language, and functional programming language.