Hierarchical Data Conversion via Anchor and Object Paths
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Solution Overview
Problem
Hierarchical data, such as JSON or XML, is inefficient for searching and querying, particularly in big data analysis, due to its flexible but less structured nature compared to relational data.
Innovation Solution
A method and system for converting hierarchical data into relational data by determining anchor paths and object paths for data objects within the hierarchical data, allowing the generation of rows in corresponding tables, thereby facilitating more efficient querying and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If hierarchical data structure is used, then data storage flexibility is improved, but searching and querying efficiency deteriorates
Solution Approach 1:
The patent segments hierarchical data into multiple relational tables based on anchor paths and object paths. Each table represents a specific segment of the hierarchical structure, allowing efficient querying within each segment while maintaining the overall flexibility of the hierarchical data model.
Solution Approach 2:
The patent introduces an intermediary conversion process that transforms hierarchical data into relational data format. This intermediary step creates a bridge between the flexible hierarchical structure and the efficient relational structure, enabling both flexibility and query performance.
2Adaptability or versatility
If hierarchical data structure is used, then data model adaptability is improved, but computational efficiency for big data analysis deteriorates
Solution Approach 1:
The patent divides the hierarchical data structure into multiple relational tables through segmentation by anchor paths and object paths. This segmentation enables parallel processing and distributed computation on relational data, significantly improving computational efficiency for big data analysis while preserving data model adaptability.
Solution Approach 2:
The patent changes the structural parameter of data representation from hierarchical to relational format. This parameter change maintains the adaptability of data modeling while enabling the use of optimized relational database engines and analytical tools for efficient big data processing.
3Adaptability or versatility
If hierarchical data structure is used, then flexibility in storing various data forms is improved, but querying efficiency deteriorates
Solution Approach 1:
The patent performs preliminary action by pre-converting hierarchical data into relational format and pre-establishing the relationship structure through anchor paths and object paths. This preliminary transformation eliminates the need for complex runtime traversals of hierarchical structures, significantly reducing querying time while maintaining storage flexibility.
Data Source
AI summary
Embodiments described herein are related to systems and methods for converting hierarchical data into relational data. In one aspect, a system obtains a data object of the set of data objects in the hierarchical data. For the data object, the system can determine an anchor path and an object path. An anchor path may indicate or may be associated with a corresponding table to generate. An object path may indicate or may be associated with a corresponding column of the table. The system can determine, from a set of candidate anchor paths, an anchor path for the data object. The system can determine, from a set of candidate object paths, an object path for the data object. The system can generate a row of the table associated with the anchor path, where the row may include a value of the data object at a column associated with the object path.