Hierarchical Data Conversion to Multi-Table RDB Format
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Solution Overview
Problem
Existing information processing systems face challenges in efficiently converting data with a tree structure, including nested hierarchies, into a format suitable for Relational Databases (RDBs), leading to increased data size, memory consumption, and performance issues, while Document Databases (DDBs) are not widely adopted due to perceived performance and stability concerns.
Innovation Solution
An information processing apparatus that converts data with a hierarchical structure into a format using multiple tables, generating first tables for each information element and integrating them based on nested structure conditions to create a second table, allowing for efficient storage and retrieval, thereby adapting to both RDB and DDB systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data with tree structure including nested structure is converted into single-table-format data and stored in RDB, then the data can be stored in RDB format, but the data size of the RDB increases due to duplications of content of data
Solution Approach 1:
The patent segments the single table into multiple tables, where each table stores specific information elements from the hierarchical data structure. This segmentation eliminates data duplications by distributing data across multiple tables with defined relationships, thereby reducing the overall data size while maintaining RDB compatibility.
Solution Approach 2:
The patent implements a nested table structure where tables can contain other tables, mirroring the hierarchical nature of the original tree-structured data. This nested approach allows the system to preserve the hierarchical relationships without duplicating data, as each nested level references parent elements through foreign keys rather than storing complete copies.
2Quantity of substance
If data with tree structure including nested structure is converted into format of a plurality of tables, then the data size is reduced, but the number of tables is increased and accordingly a large amount of memory is consumed
Solution Approach 1:
The patent merges multiple first tables that satisfy predetermined conditions into second tables, reducing the total number of tables. This merging process combines tables with similar structures or related data patterns, thereby reducing memory overhead while preserving the benefits of reduced data duplication.
Solution Approach 2:
The patent creates universal table structures that can handle multiple types of hierarchical relationships through standardized schemas. These multi-functional tables can accommodate various nested structures and relationships, reducing the need for specialized tables for each specific case and thereby reducing overall system complexity.
3Reliability
If a data model having a tree structure is implemented using an RDB that handles data of a table form, then the data can be stored in RDB, but a plurality of tables must be associated with one another in a complicated manner which makes design and update difficult
Solution Approach 1:
The patent performs preliminary conversion of hierarchical data into a normalized multi-table format before storage, establishing clear relationships and structures in advance. This preliminary structuring simplifies subsequent design and update operations, as the complex hierarchical relationships are already resolved into manageable table associations with defined foreign key relationships.
Solution Approach 2:
The patent applies different structural approaches to different parts of the data model based on their specific requirements. Rather than using a uniform complex association structure throughout, the system tailors table relationships to match the local hierarchical characteristics of each data domain, making design and updates more intuitive and manageable.
Data Source
AI summary
An information processing apparatus includes a processor. The processor converts data of a hierarchical structure including a nested structure inputted in a first data format into a second data format representing the data using a plurality of tables and stores the converted data into a storing unit. The processor generates, for each information element of the inputted data, a first table including a value of the information element, and associates each of a plurality of the first tables with a corresponding another first table according to the nested structure of the data and generates a second table by integrating the first tables that satisfy a predetermined condition among the plurality of the first tables.