Grid Plus T Tree Index for Memory Database Efficiency
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Solution Overview
Problem
Current multidimensional index structures for memory databases, such as K-D Tree, R Tree, and T Tree, face inefficiencies in memory usage and search performance, particularly in telecommunications systems where millions of records need to be processed with limited resources.
Innovation Solution
The 'Grid+T Tree' method partitions record sets into grid cells, each connected with a data barrel, and sets up a T Tree for each data unit, using a k-dimensional array for the grid directory, allowing dynamic distribution and efficient memory use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If K-D Tree is used for multidimensional indexing, then search operation is simple and index efficiency is good, but storage efficiency is too low due to limited node capacity
Solution Approach 1:
The patent combines K-D Tree's multidimensional partitioning capability with T Tree's high storage efficiency. The grid structure from K-D Tree is merged with T Tree nodes, allowing multiple data entries to be stored in each index node, thus improving memory utilization while maintaining search efficiency.
Solution Approach 2:
The patent creates a composite index structure that integrates the advantages of different tree structures. By combining K-D Tree's spatial partitioning with T Tree's node structure, it forms a new composite index method that achieves both good search performance and high storage efficiency in memory databases.
2Speed
If R Tree is used for multidimensional indexing, then disk access is reduced and search speed is faster, but memory space is wasted due to leaf node data storage requirement
Solution Approach 1:
The patent segments the index structure into grid directories and T Tree structures, allowing data to be distributed across multiple index nodes rather than concentrated in leaf nodes. This segmentation enables internal nodes to store actual data entries, improving memory utilization while maintaining fast search capability.
Solution Approach 2:
The patent changes the traditional R Tree structure by introducing a grid-based dimensional organization. Instead of relying solely on MBR overlap in leaf nodes, it uses grid coordinates to directly locate data, reducing the need for extensive leaf node storage and improving overall memory efficiency.
3Quantity of substance
If T Tree is used for indexing, then memory space is utilized efficiently with multiple keys per node, but multidimensional indexing capability is limited
Solution Approach 1:
The patent nests T Tree structures within a grid-based multidimensional framework. Each grid cell contains a T Tree, and the grid structure provides the multidimensional indexing capability. This nesting allows T Tree's efficient memory utilization to be combined with K-D Tree's multidimensional partitioning.
Solution Approach 2:
The patent makes the index structure dynamic by allowing flexible data distribution across grid cells and T Trees. The structure can adapt to different data patterns and query types, providing both efficient memory utilization and versatile multidimensional indexing capability through dynamic routing of search operations.
Data Source
AI summary
“Grid Plus T Tree” multidimensional index method in memory database partitions the whole record sets to different grid cells. Every grid data unit is connected with one data barrel, which loads records, through the grid directory. Every data unit is only mapped with one data barrel. For memory database, setting up a “T Tree” with these data units according to one field, so as to set up one “T Tree” for each data barrel according to one field. Then the grid directory can be presented by a k-dimensional (the dimension of keyword) array. The field ranges of these data units are called scale. Scale is the memory range of each “T Tree”. This invention successfully overcomes the shortcomings that the current index methods for memory database neither efficiently use the memory space nor suits multi-dimension. T Tree is much more efficient than K-D Tree or R Tree in using the memory space, and it has one more one-dimensional data structure during the process of grid partition, which can help store one field of the keyword as the index.