AI Graph Database Agents for Partitioned Consistency
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Solution Overview
Problem
Current graph database technologies are limited in their ability to perform computations on graphs efficiently, often requiring global management that leads to inefficiencies, locking access to the entire database for updates, and blocking algorithm execution, which hampers scalability and flexibility.
Innovation Solution
An artificially-intelligent graph database system that uses distributed computing to enable micro-management of graph data and algorithm execution through artificially-intelligent agents, allowing parallel processing and dynamic response to changes without locking the entire database, optimizing storage formats locally, and managing data ownership and representation autonomously.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If global management is used to ensure database consistency, then reliability is improved, but device complexity increases and productivity decreases
Solution Approach 1:
The patent divides the graph database into multiple independent partitions, each managed by a separate agent. This segmentation allows parallel processing of algorithms across different partitions without requiring global locking, thus improving productivity while maintaining consistency within each partition through local transaction management.
Solution Approach 2:
The patent introduces a new dimension of management by implementing agents at the partition level rather than traditional centralized global management. This dimensional shift enables concurrent algorithm execution across multiple partitions while maintaining consistency through distributed transaction coordination, resolving the contradiction between reliability and productivity.
2Reliability
If the entire database is locked for updates to maintain ACID consistency, then reliability is improved, but loss of time increases
Solution Approach 1:
The patent segments the database into independent partitions that can be updated concurrently. Each partition has its own agent that manages local transactions, allowing updates to proceed without locking the entire database. This reduces the time databases are locked while maintaining ACID consistency within each partition through local transaction management.
Solution Approach 2:
The patent introduces partition agents as intermediaries between the application layer and the underlying storage. These agents coordinate updates locally within partitions and manage transaction boundaries, enabling ACID consistency to be maintained without requiring global database locks, thus reducing locking duration.
3Device complexity
If a single storage format is used for the entire graph to simplify management, then device complexity is reduced, but adaptability decreases
Solution Approach 1:
The patent allows each partition to use different storage formats optimized for its specific data characteristics and access patterns. This local quality approach enables adaptability and optimization for different graph regions while the overall system maintains manageable complexity through standardized agent interfaces that abstract the heterogeneity.
4Reliability
If manual oversight is increased to manage database changes, then reliability is improved, but loss of time increases
Solution Approach 1:
The patent implements self-service through autonomous agents that automatically manage partition-level transactions, algorithm execution, and data organization. These agents make local decisions without requiring manual intervention, reducing the time associated with manual oversight while maintaining reliability through programmed consistency protocols and automated transaction management.
Data Source
AI summary
A system for providing an artificially-intelligent graph database is disclosed. The graph database has significant graph computing capabilities, which are facilitated through the integration of artificial intelligence into the graph database. The capabilities are enabled through the use of unique artificially-intelligent agents that are each responsible for a subgraph of a graph of the graph database. Each artificially-intelligent agent performs computations on the graph-structured data in each subgraph that each agent is responsible for managing. The artificially-intelligent agents may work cohesively together to provide artificial intelligence capabilities to the entire graph database. The graph database may optimize itself through intelligent local management by using the agents. Notably, the graph database may optimize and manage itself with respect to previous interactions, such as requests for data, changes to data, and algorithm execution, as well with respect to future interactions associated with the graph database, while modifying management as time progresses.