Temporal Graph Database Using Vertex-Centric Journaling
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Solution Overview
Problem
Current graph databases do not maintain temporal dimensions, providing only the latest snapshot of data and its current state, which restricts simulations and can lead to inadequate training and increased risk of susceptibility to loss, particularly in detecting transactions like seller collusion.
Innovation Solution
A system and method for generating highly scalable temporal graph databases using a vertex-centric temporal journaling schema, where events are ingested, transformed, and journaled to support time travel graph queries, enabling the identification of mutations and changes over time with low latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If current graph databases provide only the latest snapshot of data, then data storage is simple and fast, but temporal relationship analysis is lost
Solution Approach 1:
The patent segments the graph database into multiple time-point snapshots, where each snapshot represents the graph state at a specific time. This allows temporal relationships to be preserved by storing historical versions separately, resolving the contradiction between maintaining temporal information and keeping the database structure simple.
Solution Approach 2:
The patent introduces a temporal dimension to the traditional graph database structure by adding time-point metadata to graph edges and nodes. This dimensional extension enables temporal query capabilities while maintaining the core graph structure, effectively resolving the information loss issue without completely redesigning the database architecture.
2Measurement precision
If temporal graph data is stored to enable time travel queries, then risk detection accuracy improves, but query latency increases
Solution Approach 1:
The patent pre-computes and stores temporal graph snapshots at multiple time-points, so that when a time travel query is executed, the system can directly retrieve pre-prepared historical data without performing complex real-time computations. This preliminary action reduces query latency while maintaining accurate risk detection across different time periods.
Solution Approach 2:
The patent creates copies of the graph database at different time-points, allowing queries to access historical states without affecting the current state. These copies enable accurate temporal analysis while optimizing query performance by avoiding the need to reconstruct historical graphs during query execution.
3Adaptability or versatility
If machine learning models are used for computational analytics, then statistical analysis capability improves, but data management complexity increases
Solution Approach 1:
The patent introduces a temporal graph database as an intermediary layer between raw data sources and machine learning models. This intermediary structure organizes data in a way that is naturally suited for temporal relationship analysis, reducing the data management complexity that would otherwise be required to support ML analytics with temporal dimensions.
Solution Approach 2:
The patent transforms the data structure by changing parameters such as adding temporal metadata to graph edges and nodes, and organizing data into time-point snapshots. This parameter transformation makes the data more suitable for machine learning analytics while simplifying the management complexity by providing a standardized temporal data format.
Data Source
AI summary
Aspects of the present disclosure involve systems, methods, devices, and the like for generating highly scalable temporal graph databases. In one embodiment, a novel architecture is presented that enables the identification of mutation or changes in graphs. For the identification a combination of graph-based modeling and journal entry is used. Events occurring are consumed and changes are ingested, transformed for use by a graph simulation system. The changes are journaled using a vertex centric temporal journaling schema.