Derivative Graph Adjacency List Modification
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Solution Overview
Problem
Relational databases face challenges in efficiently executing graph algorithms on graph data stored in read-only formats, as they require modifications to attributes without altering the original data, and existing solutions do not effectively manage derivative graphs as snapshots or views.
Innovation Solution
A system that generates a derivative graph based on an adaptable adjacency list, allowing temporary attributes to be modified and propagated or confined depending on whether the derivative graph is defined as a view or snapshot, using a database management system to execute graph algorithms on relational databases with vertex and edge tables.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If graph algorithms are executed on read-only graph data in relational databases, then data integrity is maintained, but the ability to modify attributes during algorithm execution is lost
Solution Approach 1:
The patent segments graph data into two distinct representations: the original read-only graph data stored in the database, and a derivative graph structure created in memory that allows modifications. This segmentation enables simultaneous maintenance of data integrity and modification capability by separating the immutable source data from the mutable working copy.
Solution Approach 2:
The patent creates a derivative graph as a copy of the original graph data, allowing modifications to be made to the copy without affecting the original read-only data. The derivative graph inherits the structure and data from the original graph but exists as a separate, modifiable entity that can be used for algorithm execution.
2Stability of the object's composition
If derivative graphs are created as snapshots, then data consistency is preserved, but changes cannot be propagated to the original graph
Solution Approach 1:
The patent introduces dynamic configurability to the derivative graph system, allowing users to specify whether a derivative graph should be a snapshot (immutable) or a view (mutable with propagation). This dynamic approach enables the system to adapt to different operational requirements, switching between consistency-preserving and efficiency-optimizing modes as needed.
Solution Approach 2:
The patent changes the parameter of derivative graph mutability from a fixed state to a configurable state. By introducing a parameter that controls whether modifications are propagated back to the original graph, the system can adjust its behavior based on the specific algorithm and data requirements, optimizing both consistency and productivity.
3Productivity
If derivative graphs are created as views, then modifications can be propagated to the original graph, but data consistency may be compromised
Solution Approach 1:
The patent uses the derivative graph as an intermediary between the read-only original graph data and the algorithm execution process. When configured as a view, this intermediary allows controlled propagation of changes back to the original data, enabling productivity improvements while maintaining a layer of abstraction that can be managed to preserve consistency where needed.
4Reliability
If graph data is stored in read-only format in relational databases, then data security and integrity are enhanced, but graph algorithms requiring attribute modifications cannot be efficiently executed
Solution Approach 1:
The patent segments the graph data system into a secure read-only storage layer in the relational database and a flexible in-memory derivative graph layer for algorithm execution. This segmentation maintains data security in the database while providing ease of operation in the derivative graph where attributes can be freely modified during algorithm execution.
Solution Approach 2:
The patent creates a copy of the graph data in the form of a derivative graph that can be freely modified during algorithm execution. This copying approach maintains the security and integrity of the original read-only data in the database while providing the flexibility needed for various graph algorithms that require attribute modifications.
Data Source
AI summary
A method for executing a graph algorithm may include responding to a request from a client to execute a graph algorithm on graph data stored in a database by determining data required to execute the graph algorithm. In response to determining that a first portion of the data required to execute the graph algorithm is absent from an existing adjacency structure that includes a second portion of the data required to execute the graph algorithm, the existing adjacency structure may be modified to include the first portion of data. The graph algorithm may be executed based on the modified adjacency structure. The execution of the graph algorithm may include querying, based on the modified adjacency structure, the graph data stored in the database. Related systems and articles of manufacture, including computer program products, are also provided.