Trait Identification via Pre-computed Hash Tables
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Solution Overview
Problem
In large multi-tenant enterprise cloud environments, identifying object traits is computationally expensive due to the need to traverse extensive metadata graphs, leading to increased response times and system resource usage, especially as the number of objects and attributes grows.
Innovation Solution
Pre-computation of trait data structures at system bootstrap, creating a hash table with traits as keys and class or object IDs as values, allowing for O(1) or O(log n) lookup complexity, thereby reducing the need for real-time metadata traversal and enabling fast trait queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time metadata traversal is performed to identify object traits, then accurate trait identification is achieved, but response time increases and system resource usage increases
Solution Approach 1:
The patent pre-computes and stores trait information in data structures (hash tables, heaps) during system initialization or when metadata changes occur. This preliminary action allows the system to answer trait queries by simply looking up pre-computed results rather than traversing metadata graphs in real-time, thus resolving the contradiction between accurate trait identification and fast response time
Solution Approach 2:
The patent creates simplified copies of trait information in the form of hash tables mapping traits to class/object IDs, and heaps organizing objects by their traits. These copies enable O(1) or O(log n) lookup complexity compared to the original metadata traversal approach, maintaining accuracy while dramatically improving query speed
2Measurement precision
If real-time metadata traversal is performed to identify object traits, then accurate trait identification is achieved, but system resource usage increases
Solution Approach 1:
The system performs trait computation in advance and stores results in optimized data structures. When trait queries are received, the system simply retrieves pre-computed information without performing expensive metadata traversals, thus reducing CPU cycles, memory access operations, and energy consumption while maintaining accurate trait identification
Solution Approach 2:
The patent creates compact hash tables and heaps that store only essential trait-to-object mappings rather than full metadata graphs. These simplified copies require significantly fewer computational resources to query while preserving the ability to accurately determine object traits
3Measurement precision
If traditional metadata traversal is used for trait identification, then comprehensive trait analysis is possible, but scalability deteriorates as the number of objects and attributes increases
Solution Approach 1:
The patent segments the large metadata graph into smaller, manageable hash tables (mapping traits to class/object IDs) and heaps (organizing objects by traits). This segmentation allows the system to handle large numbers of objects and attributes by breaking down the complex traversal problem into multiple efficient lookup operations, thereby improving scalability while maintaining comprehensive trait analysis capability
Solution Approach 2:
The patent transforms the traditional single-dimension metadata traversal approach into a multi-dimensional indexing system using hash tables and heaps. This dimensional change enables the system to access trait information from multiple angles (trait-to-objects, objects-to-traits) simultaneously, improving both scalability and completeness of trait analysis as the system grows
Data Source
AI summary
The present disclosure involves systems, software, and computer implemented methods for identifying traits of an object. In one example, a set of traits is identified in an object-oriented system. A set of trait rules is identified. A trait data structure is generated. A set of classes in the object-oriented system is identified. For each class in the set of classes, a metadata structure is constructed. For each trait in the trait data structure, a trait rule in the trait computation rules structure is identified. The trait rule is applied to the particular metadata structure to generate a trait rule result. Whether the particular class has the particular trait is determined based on the trait rule result. In response to a determination that the particular class has the particular trait, the trait data structure is updated. The updated trait data structure indicates that the particular class has the particular trait.