Cognitive Database Structure Graph Recognition and Interactive Overlay
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Solution Overview
Problem
Complex database system architectures are difficult to understand and navigate due to their expansive nature, leading to undesirable trade-offs in illustration, manual errors in troubleshooting, and challenges in integrating multiple generations of hardware and diagrams.
Innovation Solution
The cognitive recognition and reproduction of structure graphs involves a system that executes a querying process to retrieve database documents, tokenizes and groups natural language text using NLP, generates feature maps from database structure graphs, detects and classifies architectural elements using neural networks, and renders interactive overlays with information about selected elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If database structure graphs are illustrated in detail to show all architectural elements and associations, then completeness of information is improved, but complexity of visualization and ease of navigation deteriorates
Solution Approach 1:
The patent applies segmentation by dividing the complex database structure graph into multiple hierarchical levels. The system segments architectural elements into different categories (e.g., storage devices, buffer pools, loggers) and organizes them in a tree-like hierarchy. This allows the complete structure to be broken down into manageable sections that can be navigated progressively, preventing information overload while maintaining completeness.
Solution Approach 2:
The patent introduces an interactive dimension to the visualization by implementing clickable nodes and overlays. When a user clicks on a node, an overlay appears providing detailed information about that specific architectural element. This transforms the static 2D graph into an interactive multi-dimensional interface where detailed information is available on demand without cluttering the main visualization.
2Adaptability or versatility
If manual troubleshooting of database architectures is performed, then flexibility in problem-solving is improved, but accuracy and error rate deteriorates
Solution Approach 1:
The patent implements feedback mechanisms by providing interactive overlays that automatically display information about selected architectural elements. When a user clicks on a node, the system provides immediate feedback showing detailed properties, associations, and status information. This automated feedback loop reduces manual errors while maintaining the flexibility of interactive exploration, as the system guides the user with accurate information without restricting problem-solving approaches.
3Loss of information
If multiple generations of hardware and diagrams are integrated into a single view, then comprehensiveness of architectural representation is improved, but clarity and ease of understanding deteriorates
Solution Approach 1:
The patent segments different generations of hardware and architectural elements into distinct nodes within the same hierarchical structure. Each generation can be visually distinguished while maintaining their relationships in the overall architecture. This allows comprehensive representation of multiple generations without creating a chaotic mixture, as the hierarchical organization provides natural grouping and separation.
Solution Approach 2:
The patent uses interactive overlays as intermediaries between the user and the complex multi-generational architecture. Instead of requiring the user to directly interpret a cluttered diagram showing all generations simultaneously, the overlay acts as a mediator that presents information in a structured, easy-to-understand format when a specific element is selected, bridging the gap between comprehensive data and clear presentation.
Data Source
AI summary
An embodiment includes executing a querying process that returns database documents containing text associated with a database environment. The embodiment tokenizes the database documents into a series of n-gram tokens and groups the tokens into topic classes using natural language processing (NLP). The embodiment also generates a feature map by applying a convolution layer to an image that depicts a database structure graph of the database environment. The embodiment detects an architectural element in the image by applying a region proposal network (RPN) to the feature map and classifies the architectural element into one of the plurality of topic classes using a trained neural network. The embodiment renders, responsive to a user selection of the architectural element, an overlay depicting information about the architectural element using text and other architectural elements that are in a same topic class as the user-selected architectural element.