Multi-Dimensional Vector Search via Hash Table Encoding
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Solution Overview
Problem
Existing data management systems face challenges in efficiently querying and indexing large volumes of data across multiple data warehouses, leading to slower processing times and increased storage requirements, particularly when dealing with high-volume relational databases, and often require sophisticated programming and expensive equipment.
Innovation Solution
The multi-dimensional real-time vector search and heuristics-backed insight engine encodes an N-dimensional profile into a single integer, allowing for spatial vector searches using modulo arithmetic and bitwise operations, enabling quick querying and analysis without the need for extensive indexing, thus reducing storage needs and computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional indexing methods are used to query large volumes of data across multiple data warehouses, then data can be organized and retrieved, but processing times increase and storage requirements expand
Solution Approach 1:
The patent merges multiple data warehouses into a single unified data structure using a hash table approach. Instead of querying across separate data warehouses with traditional indexing, the system combines all data into one hash table where records are distributed across buckets, eliminating the need for complex cross-warehouse indexing while reducing storage overhead and improving query performance.
Solution Approach 2:
The patent changes the fundamental parameter of data organization from traditional B-tree or hash index structures to a custom hash table with bucketed records. This parameter change allows for O(1) average case lookup time while reducing the index size compared to traditional methods, directly addressing both storage and speed requirements.
2Productivity
If sophisticated indexing structures are implemented to enable fast querying, then query performance improves, but device complexity and programming difficulty increase
Solution Approach 1:
The patent extracts the complex indexing logic from the database management system and implements it as a simple hash table structure. By taking out the indexing mechanism and replacing it with a straightforward hash-based bucket system, the patent achieves fast query performance without the complexity of multi-level B-trees, partitioning, or distributed indexing across multiple data warehouses.
Solution Approach 2:
The patent uses a simplified copy of the traditional index structure - a hash table that mirrors the functionality of complex indexing systems but with much simpler implementation. This copy approach allows the system to achieve indexing performance without replicating the complexity of traditional database indexing mechanisms.
3Quantity of substance
If data is stored across multiple data warehouses to accommodate large volumes of information, then data capacity increases, but query processing time increases
Solution Approach 1:
The patent merges the data from multiple data warehouses into a single unified hash table structure. This consolidation allows the system to maintain large data capacity while eliminating the time overhead of querying across multiple separate warehouses. The bucketed record structure enables parallel access across what were previously separate data silos, achieving both capacity and speed.
Data Source
AI summary
A method to improve, simplify, and facilitate multi-dimensional real time vector search and a provide a heuristics backed insight engine tool and a computer system to improve, simplify, and facilitate multi-dimensional real time vector search and provide a heuristics backed insight engine tool to provide real time, free flow, multi-dimensional search or query function of high volume, structured data to traverse to a point in generalized multi-dimensional Euclidean space without constraints of pre-identifying search dimensions.