Recursive Node Hierarchy Evaluation in Graph Data Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data access technologies face challenges in efficiently accessing and processing data stored in various graph structures, particularly in handling recursive nodes, time-dependent graphs, and anonymous graphs, which require sophisticated evaluation and processing techniques.
Innovation Solution
A system and process that utilize a generic graph service to access and process data models with recursive nodes, allowing for runtime processing and generation of relations between non-related data, independent of the graph structure, and enabling filtering and retrieval of data based on validity periods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a pull-mechanism is used to access data from legacy persistencies, then applications can access data without migration, but the data retrieval complexity increases and processing time is extended
Solution Approach 1:
The system pre-evaluates graph structures and precomputes relation paths during idle periods or batch processing, storing the results in optimized formats. When applications request data, the pre-computed relations are directly retrieved rather than computed in real-time, significantly reducing data retrieval time while maintaining full compatibility with legacy persistencies.
Solution Approach 2:
A generic graph service layer is introduced as an intermediary between applications and legacy persistencies. This service handles the complex pull-mechanism operations, relation evaluation, and data transformation, shielding applications from complexity while enabling efficient data access through optimized query processing and cached relation structures.
2Measurement precision
If sophisticated evaluation techniques are used for time-dependent graphs, then accurate runtime structure building is achieved, but processing complexity and computational resources increase
Solution Approach 1:
The graph evaluation process is segmented into distinct phases: structure validation, time-dependency checking, relation computation, and runtime instantiation. Each phase handles specific aspects independently with optimized algorithms, reducing overall complexity while maintaining accurate reconstruction of time-dependent graph structures at runtime.
Solution Approach 2:
The system transforms time-dependent graph parameters into standardized runtime representations by evaluating validity periods, temporal relations, and state changes. Complex time-dependent parameters are converted into simplified runtime attributes that preserve accuracy while enabling efficient processing through parameter normalization and pre-computed temporal metadata.
3Ease of manufacture
If generic graph services are used to process non-relational data models, then coding redundancy is reduced, but the processing overhead increases
Solution Approach 1:
The generic graph service applies partial processing only to the portions of non-relational data models that require relation-based operations. Instead of fully transforming all data, the service selectively processes nodes and edges that benefit from generic graph services, maintaining original data structures where appropriate and reducing unnecessary processing overhead while still achieving code redundancy reduction.
Data Source
AI summary
Systems and processes may access data in various graph structures, often including data models that do not partially or fully utilize relations such as hierarchical graphs. For example, software for processing this non-relational data identifies a data model that includes a plurality of nodes independent of relations, each non-root node storing an identifier of the particular node's parent. The software then processes the data model using a generic graph service that utilizes nodes and relations. In some cases, such data models may include hierarchical graph structures and one or more of the nodes may be a recursive node.