Event-Driven Graph Database Hydration from External Data
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Solution Overview
Problem
Conventional methods for hydrating graphs from underlying file and object data are manual, time-consuming, error-prone, and do not provide near real-time updates, leading to delays and reduced effectiveness in graph-based workflows.
Innovation Solution
A system and method for automatically hydrating graphs by emitting events when data is created, modified, or deleted, using a graph proxy to listen for these events and interact with a graph database to create, modify, or delete records, with schema and rule generation facilitated by machine learning algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual, bespoke efforts are used to extract and load data into graph databases, then users can customize the hydration process, but the process becomes time-consuming and delays workflow execution
Solution Approach 1:
The system enables self-service automation where the graph database hydration process automatically extracts data from external sources, transforms it using configured schemas and rules, and loads it into the graph database without requiring manual intervention. This eliminates the time-consuming manual effort while preserving customization through pre-configurable schemas, rules, and parameters that users can set up once and reuse.
2Quantity of substance
If batch processing is used for data extract and load logic, then processing can be performed on large datasets, but near real-time updates of the graph are not provided
Solution Approach 1:
The system dynamically adapts the processing mode based on requirements, supporting both batch processing for large historical datasets and near-real-time processing for current data updates. The architecture allows flexible switching between processing modes, enabling users to process terabytes of historical data in batch while simultaneously handling real-time data streams with low latency through the same unified platform.
3Device complexity
If existing knowledge graphs and ontologies are not fully utilized, then implementation complexity is reduced, but terminology confusion occurs and the ability to take full advantage of graph algorithms is diminished
Solution Approach 1:
The system performs preliminary action by pre-configuring schemas, rules, and parameters that encode domain knowledge and terminology standards before the actual data hydration process. These pre-configured elements ensure consistent terminology and relationships are applied throughout the graph database, eliminating confusion and enabling effective use of graph algorithms without requiring complex implementation decisions during runtime.
4Adaptability or versatility
If custom solutions are crafted for each data extraction and loading task, then specific requirements can be met, but the process becomes error-prone and difficult to maintain
Solution Approach 1:
The system implements a universal platform that handles multiple data extraction, transformation, and loading scenarios through a single unified interface. The configurable schemas, rules, and parameters allow the same system to accommodate diverse requirements across different data sources and graph database targets, eliminating the need for separate custom solutions while maintaining adaptability through configuration rather than code changes.
Data Source
AI summary
One example method includes receiving, by a graph proxy, an event from an event generator, and the event includes information about an IO and information about data affected by the IO, comparing, by the graph proxy, the data to a schema, when the data is determined by the graph proxy to map to the schema, identifying, by the graph proxy, a rule that is associated with the event, and the rule specifies performance of an action when a condition is met, and when the condition is met, performing, by the graph proxy, the action, and the action is performed with respect to a graph.