Dynamic AI Graph Analytics Templates for Tool Compatibility
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Solution Overview
Problem
Existing graph analytics tools require tool-specific coding languages, making them time-consuming and expensive for new users, and often necessitate extensive data knowledge to identify nodes and relations.
Innovation Solution
The development of dynamic AI-supported graph-analytics self-learning templates, which include predefined template skeletons with dynamic nodes and guided videos, utilize a cognitive AI engine to identify nodes and relationships, and a BERT-based Transformer engine to adapt template code to tool-specific syntax.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If graph analytics tools use tool-specific coding languages, then they can provide precise control and customization, but they become time-consuming and require extensive coding experience
Solution Approach 1:
The patent implements a universal template system that can be adapted across multiple graph analytics tools (Neo4j, Amazon Neptune, Microsoft Azure Cosmos DB). The templates use a standardized syntax that translates to tool-specific queries, allowing one template to serve multiple tools without requiring separate coding for each platform.
Solution Approach 2:
The patent introduces an intermediary layer (the template system with translation engine) that sits between the user and the tool-specific coding requirements. This intermediary automatically translates high-level template definitions into tool-specific queries, shielding users from coding complexity while maintaining compatibility with various tools.
2Measurement precision
If graph analytics tools require extensive data knowledge to identify nodes and relations, then they can ensure accurate data modeling, but they become inaccessible to new users
Solution Approach 1:
The patent implements self-service mechanisms where the system automatically performs tasks that traditionally required expert knowledge. The template system automatically identifies nodes and relations from dataset schemas, and the AI assistant provides context-aware suggestions, allowing users to perform accurate data modeling without extensive domain expertise.
Solution Approach 2:
The patent applies preliminary action by pre-defining template structures that encapsulate best practices for node and relation identification. These templates are prepared in advance with common data modeling patterns, so users don't need to start from scratch or possess deep expertise - the heavy lifting of accurate data identification has already been done in the template design phase.
3Productivity
If graph analytics templates are predefined, then they can reduce coding time, but they may lack flexibility for custom requirements
Solution Approach 1:
The patent implements dynamic templates that can adapt to custom requirements. The template system allows users to modify predefined templates through a declarative syntax, and the AI assistant dynamically adjusts template parameters based on specific dataset characteristics and user requirements. This maintains the speed of template-based development while enabling customization when needed.
Solution Approach 2:
The patent enables customization through parameter changes rather than structural rewrites. Users can modify template parameters (such as node labels, relation types, and query conditions) to adapt predefined templates to custom requirements. The translation engine automatically adjusts the generated code based on these parameter changes, maintaining both productivity and adaptability.
4Adaptability or versatility
If multiple graph analytics tools are licensed, then they can provide diverse functionality, but they increase cost and reduce compatibility
Solution Approach 1:
The patent creates a universal template system that provides consistent functionality across multiple graph analytics tools. Instead of requiring separate tool-specific implementations, the same template syntax and structure work across Neo4j, Amazon Neptune, Microsoft Azure Cosmos DB, and other tools, reducing the need to license and integrate multiple specialized solutions.
Solution Approach 2:
The patent merges the functionality of multiple tool-specific implementations into a single unified template system. The translation engine combines knowledge of various tool APIs and query languages into a unified approach, allowing users to work with a single system that adapts to different tools rather than managing multiple separate tool integrations.
Data Source
AI summary
Methods and systems described herein for addressing issues associated with varying graph analytics tools that require different tool-specific coding languages. An artificial intelligence (AI) sub-system of various modules extracts metadata from a dataset and identifies nodes and relationships in the dataset using the metadata. The dataset is matched with a corresponding graph-analytics template in a data store, and a dynamic template modifier modifies the corresponding graph-analytics template. In some examples, the AI system generates smart guided videos with logical breakpoints that are embedded along with templates for quick learning and to build faster graphical analytics. The AI system includes a dynamic template modifier and a cognitive smart AI engine that includes a graph.