Database Access Using Space-Filling Curves and Sparse Trees
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Solution Overview
Problem
Existing database management systems face inefficiencies in scaling and indexing large datasets, particularly in big data environments, due to global indexing bottlenecks and slow query performance when handling many columns and rows, which leads to unmanageable systems in enterprise networks and modern Internet environments.
Innovation Solution
The use of space-filling curves and sparse trees to efficiently access data, where a space-filling curve traverses multidimensional data structures and a sparse tree acts as a cache to track data intersections, allowing for optimized query performance by adjusting search paths and limiting boundaries, thereby reducing unnecessary data searches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If global indexing is used to manage data across multiple nodes, then data retrieval capability is improved, but system performance deteriorates due to indexing bottlenecks
Solution Approach 1:
The patent divides the global indexing problem into local indexing units at each node. Each node maintains its own index independently rather than requiring a centralized global index, which eliminates the indexing bottleneck while preserving data retrieval capability across the distributed system.
Solution Approach 2:
The patent introduces space-filling curves as an intermediary mechanism that maps multi-dimensional data relationships into one-dimensional sorted sequences. This intermediary structure enables efficient data location and retrieval across distributed nodes without requiring complex global indexing operations.
2Productivity
If data is stored in sorted order to eliminate syncing overhead, then system scalability is improved, but query performance deteriorates when handling multiple columns
Solution Approach 1:
The patent transforms multi-dimensional query requirements into one-dimensional sorted data access by applying space-filling curves. The curve mapping converts multi-column query conditions into a single sorted sequence that can be efficiently traversed, maintaining both scalability and query performance.
Solution Approach 2:
The patent changes the parameter representation by encoding multi-dimensional coordinates into one-dimensional values using space-filling curve mappings. This parameter transformation allows sorted data storage to satisfy multi-column query requirements without sacrificing query performance.
3Measurement precision
If multiple columns are sorted sequentially to satisfy query requirements, then query accuracy is improved, but processing time deteriorates for large datasets
Solution Approach 1:
The patent performs preliminary sorting of data using space-filling curve mappings before queries are executed. By pre-organizing data according to the curve-based one-dimensional sequence, the system eliminates the need for sequential multi-column sorting during query processing, significantly reducing processing time while maintaining query accuracy.
Data Source
AI summary
Disclosed is an approach for improved access of databases using a space-filling curve, such as a z-order curve, and a sparse tree. The space-filling curve traverses every point in n-dimensional space of a multidimensional data structure. The sparse tree can be implemented as a cache to store which rows of the multidimensional data structure have actual data. The sparse tree may have one or more nodes merged into a single node, The sparse tree may have one or more node limits at each node that limit node spawning. Node counters track how many rows containing data not mapped correspond to each node. As the multidimensional data structure is searched, the search path is adjusted by reseeking back to rows that are located in the sparse tree. Further, the search path is adjusted by reseeking back to rows that are located within a query box.