Hyper-folding Data Tree for Uniform Interaction Feed
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional entity relational (ER) model-based data systems require a large amount of memory and complex data joins to capture and analyze user events, making them inefficient for encapsulating context, especially in data science applications where hundreds of tables are needed to track different events.
Innovation Solution
The implementation of an event analytical data structure using a hyper-folding process that generates a single data tree from multiple context data trees, reducing the need for complex joins and memory usage by encapsulating context in a single record.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional entity relational model-based data systems are used to capture user events, then data can be organized in structured tables, but memory usage increases and complex data joins are required
Solution Approach 1:
The patent merges multiple data tables and complex join operations into a single event analytical data structure that captures user events, device information, and contextual data in one unified format, eliminating the need for multiple data joins while maintaining data integrity and capture capability
Solution Approach 2:
The patent segments the complex relational data model into hierarchical data trees with parent-child relationships, where each node represents a specific data entity (user, device, event) and their relationships are explicitly defined in the tree structure rather than through joins
2Loss of information
If multiple data tables are used to track different user events, then comprehensive event context can be captured, but memory usage increases
Solution Approach 1:
The patent combines multiple data tables containing user events, device information, and contextual data into a single event analytical data structure that preserves complete event context while reducing memory footprint by eliminating redundant data storage across multiple tables
Solution Approach 2:
The patent implements a nested hierarchical structure where user data contains device information, which in turn contains event data, creating a compact nested representation that reduces overall memory usage while maintaining complete contextual information about user events
3Ease of manufacture
If conventional ER models with numerous tables are used, then systematic data analysis is possible, but data searching speed decreases
Solution Approach 1:
The patent segments data into a hierarchical tree structure with clear parent-child relationships, allowing systematic analysis through tree traversal while enabling faster searching by directly navigating to relevant data nodes rather than performing complex joins across multiple tables
Solution Approach 2:
The patent transforms the two-dimensional relational table structure into a multi-dimensional hierarchical tree structure, adding the dimension of hierarchical levels and enabling faster data access through path-based navigation while maintaining systematic analysis capabilities
Data Source
AI summary
Embodiments of the present invention provide a computer system, a computer program product, and a method that comprises generating a context data tree for each variable in a plurality of variables based on a received input; determining data folding points for each generated context data tree; conducting a hyper-folding process on the determined data folding points in each context data tree, wherein the hyper-folding process converts each generated context data tree into a single data tree; and automatically loading the single data tree into an application.