Dynamic AI-Supported Graph Analytics Templates for Cross-Tool Coding
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Graph analytics tools require tool-specific coding languages, making them time-consuming and expensive for new users, and are not compatible with each other, necessitating extensive coding experience and maintenance.
Innovation Solution
A vendor-agnostic system using a BERT-based AI transformer engine generates dynamic nodes and templates with logical grouping, enabling seamless template adoption across tools through a cognitive AI engine that modifies code and embeds data for offline analytics, supported by smart guided videos with logical breakpoints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If graph analytics tools use tool-specific coding languages, then they can provide precise control and customization, but they require extensive coding experience and time to develop and maintain
Solution Approach 1:
The patent uses template copying where pre-defined graph analytics templates can be replicated and reused across different tools. Instead of creating analytics from scratch with tool-specific code, users can copy existing templates and modify them, significantly reducing the coding effort and expertise required while maintaining the ability to customize analytics when needed.
Solution Approach 2:
The patent creates universal templates that work across multiple graph analytics tools simultaneously. A single template can be adopted by different tools (Neo4j, Gephi, Cytoscape, etc.) without requiring separate tool-specific implementations, thereby reducing overall coding complexity while providing precise control through the template's configurable parameters.
2Adaptability or versatility
If different graph analytics tools are used, then users can choose the best tool for specific tasks, but the tools are not compatible with each other and require extensive coding experience
Solution Approach 1:
The patent develops universal templates that are compatible with multiple graph analytics tools including Neo4j, Gephi, Cytoscape, and others. These templates can be deployed across different tools without modification or minimal adaptation, enabling users to leverage the strengths of different tools while maintaining consistency in analytics implementation and eliminating the need to recreate templates for each tool.
Solution Approach 2:
The patent introduces an intermediary layer (the universal template system) that mediates between the user's analytics requirements and the tool-specific implementations. This intermediary handles the complexity of tool-specific coding languages and compatibility issues, allowing users to work with a unified template interface while the system manages the translations and adaptations to various tool-specific formats.
3Reliability
If licensed graph analytics tools are used, then professional functionality is available, but analytics become expensive and require extensive coding experience
Solution Approach 1:
The patent enables copying and reuse of proven, reliable graph analytics templates across different tools and organizations. Instead of requiring users to develop custom analytics from scratch (which is time-consuming and error-prone), they can copy established templates that have already been validated for reliability, while the template system handles the complexity of implementation details.
Solution Approach 2:
The patent implements self-service capabilities where users can independently select, customize, and deploy graph analytics templates without requiring extensive coding expertise. The template system provides user-friendly interfaces for configuration and automatic handles the complex coding and tool-specific adaptations, making professional-grade analytics accessible to users with minimal training.
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.