Nested Table Query via Tree Structure Conversion
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Solution Overview
Problem
The query efficiency of tables, especially those with nested structures, is low due to the complexity of manually specifying header and nested table positions, making it difficult to analyze and retrieve data effectively.
Innovation Solution
Converting tables into tree structures through absorbing and merging processing to create a target tree structure that allows for quick querying of content without needing to specify header or embedded table positions, enabling efficient data retrieval based on node connections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If tables with nested structures are used to store complex data, then data expression capability is improved, but query efficiency deteriorates
Solution Approach 1:
The patent segments the complex nested table into multiple independent tree structures, where each tree represents a specific nested table. This segmentation allows the query system to work with simpler, non-nested tree structures instead of complex nested tables, improving query efficiency while maintaining the ability to express complex data relationships through multiple connected trees.
Solution Approach 2:
The patent introduces tree structures as an intermediary representation layer between the nested table data and the query operation. By converting nested tables into trees with parent-child node relationships, the system gains a more query-friendly structure that maintains data expression capabilities while enabling efficient content-based querying without manual position specification.
2Manufacturing precision
If manual specification of header and nested table positions is required, then data structure accuracy is improved, but operation complexity increases
Solution Approach 1:
The patent implements self-service by enabling the tree structure to automatically identify and represent data relationships through its inherent parent-child node connections. The tree structure inherently captures the hierarchical relationships and data associations without requiring manual position specification, thus maintaining data structure accuracy while eliminating operational complexity.
Solution Approach 2:
The patent replaces the mechanical system of manual position specification with an automated tree-based representation system. Instead of manually specifying header and nested table positions, the system uses tree node relationships (parent-child connections) to automatically represent data structure, thereby reducing operation complexity while preserving structural accuracy.
3Adaptability or versatility
If nested tables are used to express complex relationships, then data modeling capability is improved, but query difficulty increases
Solution Approach 1:
The patent segments complex nested table relationships into multiple simpler tree structures that can be independently queried. Each tree handles a specific aspect of the data relationship, making detection and measurement easier while collectively maintaining comprehensive data modeling capability through their interconnections.
Solution Approach 2:
The patent uses tree structures as intermediaries that simplify the detection and measurement of data relationships. The tree's inherent hierarchical structure and node connections provide a more query-friendly representation compared to nested tables, reducing query difficulty while preserving the ability to model complex relationships through multiple trees.
Data Source
AI summary
A method and apparatus for processing a table, a device, a storage medium and a product. An implementation of the method comprise: receiving a content query request for a target table; acquiring a target tree structure of the target table according to the content query request; where, the target tree structure is obtained by performing absorbing processing and merging processing on at least one target cell in the target table; acquiring to-be-queried content in the content query request; and querying target content matching the to-be-queried content from the target tree structure.