Time-Aware Graph Architecture for Chronological Data Queries
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Solution Overview
Problem
Conventional graphs lack the ability to provide a chronological view of how a system evolved over time, are cumbersome to implement for complex operations, and suffer from a lack of standardization, making data queries complex and query performance low.
Innovation Solution
Introduce a time aware graph architecture that incorporates time as a dimension, using N-dimensional matrices to model connected data, with entities and attributes having similar structures, allowing for time-based queries and representations of graph data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional graphs (RDF or property graphs) are used to model connected data, then the current state of a system can be represented, but the graphs cannot provide a chronological view of system evolution over time
Solution Approach 1:
The patent adds a time dimension to the traditional graph structure by introducing temporal descriptors (timestamp, validity start time, validity end time) to entities and relationships. This transforms static graphs into temporal graphs that can represent system evolution chronologically without fundamentally changing the core graph model, effectively adding a fourth dimension (time) to the traditional three-dimensional graph representation.
2Adaptability or versatility
If property graphs are used to model complex operations and data, then complex data can be modeled more effectively, but there is a lack of standardization in node, relationship and property schemas
Solution Approach 1:
The patent creates a universal temporal graph model that can handle multiple types of data and operations through a standardized schema. The model uses consistent entity and relationship structures with temporal descriptors that can represent various complex operations (insertions, updates, deletions, validations) uniformly, eliminating the need for different schemas for different operations while maintaining high adaptability for complex data modeling.
Solution Approach 2:
The patent introduces temporal parameters (timestamp, validity_start_time, validity_end time) as standardized attributes for all entities and relationships. This parameter extension allows the graph model to adapt to complex temporal operations while maintaining schema standardization, as the same parameter structure can represent different types of temporal behaviors through parameter values rather than structural changes.
3Reliability
If graphs are updated over time by adding, inserting, amending, or removing nodes/edges, then the graphs can represent the current state of a system, but query performance deteriorates and queries become complex
Solution Approach 1:
The patent pre-organizes temporal graph data by maintaining chronological order of temporal descriptors and creating temporal indexes during data insertion and update operations. This preliminary organization allows the system to efficiently retrieve historical states and perform time-based queries without scanning the entire graph, significantly improving query performance while maintaining accurate current state representation.
Solution Approach 2:
The patent segments temporal graph data into discrete temporal events (insertions, updates, deletions) with specific timestamps and validity periods. This segmentation allows the query system to process only relevant temporal segments based on query time ranges, rather than evaluating the entire graph history, thereby improving query performance while maintaining reliable current state representation.
4Loss of information
If attempts are made to merge graphs with time series databases to achieve time awareness, then chronological representation can be achieved, but the implementation becomes cumbersome and ineffective
Solution Approach 1:
The patent merges the temporal modeling capabilities directly into the graph database structure by integrating temporal descriptors into entity and relationship definitions. This unified approach combines graph connectivity with temporal sequencing in a single data model, eliminating the need for separate time series database systems and complex inter-system integrations, thereby achieving chronological representation without cumbersome implementation.
Data Source
AI summary
A graph is disclosed. A graph includes entities that are represented by N-dimensional matrices. Each of the entities is associated with one or more attributes and each attribute has its own dimension. The graph can represent time aware graph entity changes, time aware graph relationship changes, and supports time aware queries.