Multi-layered Database Architecture for Scalable Knowledge Representation
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Solution Overview
Problem
Existing knowledge representation systems face scalability challenges and high I/O latency when handling large volumes of data, particularly in graph databases, due to non-linear performance scaling and the need for intense computations, which hinders efficient information retrieval and management.
Innovation Solution
The implementation of an object-oriented graph database (O2GDB) system with a multi-layered architecture, comprising an object database layer, a graph database layer, and a backend database layer, allowing for flexible and scalable knowledge representation by decomposing objects and relationships into nodes and edges with key-value pairs, enabling efficient data storage, retrieval, and manipulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If graph databases are used to store large volumes of data, then the capability to store enormous volume of large, dynamic, and sparse datasets is improved, but I/O latency increases and scalability deteriorates due to non-linear performance scaling
Solution Approach 1:
The patent segments the graph database into multiple partitions distributed across different nodes. Each partition stores a subset of the graph data, allowing parallel access and reducing I/O latency. The segmentation enables the system to handle large volumes of data without proportionally increasing access time, as queries can be executed simultaneously across multiple partitions.
Solution Approach 2:
The patent introduces a multi-dimensional storage architecture that organizes graph data along multiple dimensions (e.g., vertex-based partitioning, edge-based partitioning, property-based partitioning). This dimensional organization allows the system to optimize data access patterns for different query types, reducing I/O latency while maintaining the ability to store enormous volumes of data.
2Quantity of substance
If graph databases are used to store large volumes of data, then the capability to store enormous volume of large, dynamic, and sparse datasets is improved, but scalability worsens due to non-linear performance scaling
Solution Approach 1:
The patent divides the graph database into independently manageable partitions that can be scaled horizontally. Each partition can be added, removed, or modified without affecting the entire system, enabling linear scalability. As data volume increases, new partitions can be created and distributed across additional nodes, maintaining performance while increasing capacity.
Solution Approach 2:
The patent implements a universal graph database architecture that can handle various types of graph operations (storage, retrieval, traversal, analysis) across distributed partitions. This multi-functional design allows the system to scale efficiently by adding standardized nodes that can perform multiple operations, avoiding non-linear performance degradation.
3Adaptability or versatility
If intense computations are performed for knowledge representation, then the capability to handle complex relationships is improved, but I/O latency increases
Solution Approach 1:
The patent pre-computes and stores intermediate results of complex graph operations (e.g., path calculations, connectivity analysis, community detection) in the database. When queries are executed, the system can retrieve these pre-computed results directly without performing intensive computations in real-time, significantly reducing I/O latency while maintaining the ability to handle complex relationships.
Solution Approach 2:
The patent implements continuous background computation that performs complex graph analyses asynchronously. While the database stores and retrieves data efficiently, intensive computations run continuously in the background, updating cached results and maintaining complex relationship information without blocking I/O operations. This separation allows both complex relationship handling and low-latency data access to occur simultaneously.
Data Source
AI summary
Knowledge representation in a multi-layered database includes systems and methods for storing and retrieving data in the multi-layered database. The multi-layered database includes an object database layer that stores objects corresponding to real world entities and each object is associated with object properties, object relationships, and relationship properties. Underlying the object database layer is a graph database layer that stores nodes, node properties associated with the nodes, edges, and edge properties associated with the edges, wherein the nodes correspond to objects, node properties are indicative of the object properties, the edges are indicative of the object relationships, and the edge properties are indicative of the relationship properties of the object. Further, underlying the graph database layer is a backend database layer that stores corresponding data and metadata. Data can be stored and retrieved from the multi-layered database by composing and decomposing information between layers.