Declarative Graph Modeling for Low-Code Multi-Source Analytics
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Solution Overview
Problem
Existing graph data modeling systems lack a non-programmatic, integrated, light-weight solution for encoding formats, interfacing with multiple data-sources declaratively, visual declarative design, handling delta load/offload, and generating sub-graphs, while also failing to support low-code/no-code development for graph analytics.
Innovation Solution
A declarative graph-modeling process that supports visual and declarative modeling of graph databases, graph-physicalization, and automatic generation of sub-graphs based on user entitlements, using a graph-pipeline for low-code graph file formats and a machine-learning pipeline for analysis, enabling a low-code/no-code development platform.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional code-heavy graph modeling approaches are used, then graph analytics capabilities can be achieved, but the system complexity and development difficulty increase significantly
Solution Approach 1:
The patent introduces a visual declarative modeling interface as an intermediary between users and the graph database system. This interface allows users to define graph models through visual drag-and-drop operations and declarative specifications rather than writing complex code, thereby reducing development difficulty while maintaining system capabilities
Solution Approach 2:
The system automatically generates execution semantics, sub-graphs, and data loading strategies based on user-defined declarative models. The graph processing engine self-configures and optimizes the model execution without requiring manual programming, reducing both development effort and system complexity
2Adaptability or versatility
If multiple data sources are integrated with uniform semantics, then data comprehensiveness improves, but the interfacing complexity increases
Solution Approach 1:
The patent implements a universal declarative modeling interface that can connect to multiple heterogeneous data sources (relational databases, NoSQL databases, data lakes, etc.) through a common abstraction layer. This single interface handles various data sources uniformly, enabling versatile integration without increasing interfacing complexity for users
3Ease of operation
If declarative visual modeling is implemented, then user accessibility improves, but the automation extent for generating execution semantics decreases
Solution Approach 1:
The system pre-defines execution semantics, data loading strategies, and graph processing configurations within the declarative model specification. By performing these automation preparations in advance during model definition, the system maintains high user accessibility while ensuring that execution semantics are automatically generated and configured without requiring manual intervention during runtime
Data Source
AI summary
Disclosed herein is a declarative modeling paradigm for a graph model, graph-physicalization, delta load/offload, automatic generation of sub-graphs based on user entitlements, a graph-pipeline for low-code graph file formats, and machine-learning pipeline for analysis of the graph model. In a particular aspect, a computer-implemented process includes generating a graphical user interface comprising one or more tools configured to allow a user to build and visualize a graph model based on data, obtaining the data from one or more sources, receiving input from the user via the graphical user interface, where the input comprises a request to create at least two nodes representing logical entities within the data and at least one edge representing one or more relationships between the logical entities, generating the graph model based on the input from the user, analyzing the data using the graph model, and output results based on the analyzing.