Two-Tier Indexing for Multidimensional Data Retrieval
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Solution Overview
Problem
Current database systems are inefficient in storing and querying multi-dimensional data, such as satellite image data, due to high maintenance overheads and slow query response times caused by the need for numerous I/O operations, as traditional file systems and relational databases struggle with multi-dimensional queries.
Innovation Solution
A method and system that utilize two-tier indexes, where a primary index is created based on multiple dimensions to retrieve data units and a secondary index is created based on specific dimensions to retrieve data blocks, enhancing I/O efficiency by allowing quick location of target data through two-layer retrievals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional file systems are used for data storage, then data can be stored, but multi-dimensional query efficiency is poor and I/O operations are excessive
Solution Approach 1:
The patent segments the index structure into two distinct levels: a first index for coarse-grained data unit retrieval and a second index for fine-grained data block retrieval. This segmentation allows the system to quickly eliminate large portions of data using the first index, then precisely locate target data using the second index, thereby reducing overall query time and I/O operations compared to traditional single-level indexing approaches.
Solution Approach 2:
The patent introduces a hierarchical dimension to the traditional flat index structure by creating a two-tier indexing system. The first index operates at the data unit level while the second index operates at the data block level within those units. This dimensional transformation enables multi-dimensional queries to be processed more efficiently by leveraging the hierarchical structure to reduce the search space at each level.
2Quantity of substance
If data is distributed across many files to handle large data amounts, then storage capacity increases, but maintenance overhead increases
Solution Approach 1:
The patent creates a universal two-tier index structure that can handle multiple query dimensions and data types through the same framework. The first index can organize data units by various dimensions (spatial, temporal, etc.), while the second index provides consistent block-level access across all data units. This multi-functional index structure reduces maintenance overhead compared to having separate indexing mechanisms for different query types and data distributions.
3Measurement precision
If multi-dimensional query conditions are applied, then query precision improves, but query complexity increases
Solution Approach 1:
The patent performs preliminary organization of data into hierarchical units (data units containing multiple data blocks) with corresponding two-level indexes before queries are executed. The first index pre-organizes data units by various dimensions, and the second index pre-organizes blocks within units. When multi-dimensional queries are received, this preliminary structure allows the system to efficiently apply multiple query conditions by sequentially filtering through the two index levels, reducing the complexity of processing multi-dimensional conditions compared to scanning unorganized data.
Data Source
AI summary
The present disclosure relates to methods and systems for storing and querying data. According to the embodiments of the present invention, two-layer indexes are created for multi-dimension data, wherein the primary index is created based on two or more dimensions to retrieve respective data units of the data, while the secondary index is created based on specific dimensions to retrieve respective data blocks in the data unit. Correspondingly, when receiving a multi-dimension query request for data, the primary retrieval first determines a data unit including the target data based on a primary index, and then the secondary retrieval quickly locates a data block including the target data based on the secondary index. In this way, the multi-dimension retrieval can be efficiently performed. Moreover, by appropriately setting the size of a smallest data block, the I/O efficiency of data access will be significantly enhanced.