Reorganized Classification Tree for Data Search
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Solution Overview
Problem
Conventional data management techniques, such as tree-type classifications, face significant burdens and pressures during data maintenance and searching, particularly when dealing with large volumes of data, as they often require repetitive searches across different sub-classifications, increasing the load on search engines and data maintenance.
Innovation Solution
A method and system that generate a reorganized classification structure tree from an initial classification structure tree, allowing for flexible data storage and search by determining a new root node and child nodes, reducing unnecessary searches and alleviating the burden on search engines and data maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If conventional tree-type classification is used to manage data, then data can be organized in a structured manner, but the search engine burden and data maintenance pressure increase significantly when dealing with large volumes of data
Solution Approach 1:
The patent segments the monolithic tree-type classification into multiple inverted index structures, where each index corresponds to a specific classification dimension. This allows the search engine to query only relevant segments rather than traversing the entire tree structure, significantly reducing search burden while maintaining organized data access.
Solution Approach 2:
The patent introduces an inverted index as an intermediary layer between the tree-type classification and the search engine. This intermediary structure pre-processes and organizes data by classification dimensions, enabling efficient queries without requiring the search engine to traverse the original tree structure directly.
2Stability of the object's composition
If conventional tree-type classification is used to manage data, then data can be organized in a structured manner, but unnecessary repetitive searches occur across different sub-classifications
Solution Approach 1:
The patent performs preliminary action by pre-building inverted indexes that map classification dimensions to data elements before searching occurs. This pre-processing eliminates the need for repetitive traversals during search operations, as the search engine can directly query the pre-organized index structures.
Solution Approach 2:
By segmenting the classification structure into multiple inverted indexes based on different dimensions, the patent enables the search engine to target specific segments rather than performing full-tree traversals, thereby eliminating unnecessary repetitive searches.
3Stability of the object's composition
If conventional tree-type classification is used to manage data, then data can be organized in a structured manner, but the burden on data maintenance increases with large volumes of data
Solution Approach 1:
The patent segments the data maintenance task into multiple independent inverted indexes rather than maintaining a single large tree structure. This segmentation allows for more granular and flexible maintenance operations, where individual indexes can be updated or rebuilt without affecting the entire data structure.
Solution Approach 2:
The patent creates inverted index copies of the original tree structure, organized by different classification dimensions. These copies enable efficient querying and maintenance operations without modifying the original tree structure, simplifying data maintenance while preserving the organized structure.
Data Source
AI summary
The present disclosure provides a method and an apparatus for storing data classification structure. Based on an initial classification structure tree, a reorganized classification structure tree that stores data classifications structure differently from that of the initial classification structure is generated. As the reorganized classification structure tree is flexible, when maintaining data and searching data by using the search engine, the present techniques may quickly find the desired data, thereby reducing the heavy burden of the search engine under the conventional techniques to conduct data search and high pressure of conducting data maintenance and data search. Further, the present techniques may not only reduce the burden of the search engine, but also relieve the pressure for maintaining data and searching data.