Query Generation Apparatus Schema Association Accuracy
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Solution Overview
Problem
The challenge lies in accurately associating items from open data with user-defined output target tables, especially when there are a large number of available tables and general names, leading to erroneous associations and increased time and resource requirements for determining appropriateness.
Innovation Solution
A query generation apparatus determines a main category schema and subcategory schema based on attribute names, using similarity calculations and additional information to narrow down association candidates, thereby reducing errors in query generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If tables are sequentially determined from all available tables in open data to associate with output target table, then complete data coverage is achieved, but error rate in association increases when there are large number of tables and general item names
Solution Approach 1:
The patent segments the large set of available tables into smaller subsets based on category schemas. Instead of processing all tables sequentially, the system divides them into manageable groups (e.g., population tables, economic tables, geographic tables) and processes each subset separately, thereby reducing the complexity of association determination while maintaining comprehensive coverage.
Solution Approach 2:
The patent introduces category schemas as intermediary structures that mediate between output target tables and available data tables. Category schemas act as classification frameworks that organize tables by subject matter, enabling more accurate matching by first associating items with appropriate category schemas and then selecting tables from those categorized groups, rather than directly matching items against all tables.
2Reliability
If appropriateness of associations is determined using information other than item names, then association accuracy improves, but time and resource consumption increases
Solution Approach 1:
The patent performs preliminary organization of available tables into category schemas before the actual association process. By pre-classifying tables according to their subject matter and structure, the system prepares the data in advance, so that during query generation, the system only needs to search within relevant category schemas rather than analyzing all tables from scratch, thus reducing real-time computational burden while maintaining accurate associations.
3Loss of information
If all available tables are processed to ensure complete data coverage, then data completeness is achieved, but calculation amount and processing time increase
Solution Approach 1:
The patent segments the processing task by organizing tables into category schemas and only processing relevant subsets. Instead of uniformly processing all available tables, the system divides the workspace into categorized segments and selectively processes only those segments that are relevant to the current query requirements, ensuring data completeness while improving processing efficiency.
Solution Approach 2:
The patent implements partial processing by determining association appropriateness based on relevant features from category schemas rather than exhaustively analyzing all possible table attributes. The system performs sufficient processing to achieve accurate associations without unnecessary excessive computation, balancing completeness with efficiency by focusing on the most discriminating features for each category schema.
Data Source
AI summary
A non-transitory computer-readable recording medium stores therein a query generation program that causes a computer to execute a process including: determining, from a plurality of category schemas each being a set of attribute schemas, a main category schema to be associated with an output target table that defines acquisition target data, based on attribute names of a plurality of output items contained in the output target table; determining a subcategory schema to be associated with an un-associated item, which is an output item that is not associated with the main category schema among the plurality of output items, from category schemas that are associated with respective features of a plurality of attribute schemas contained in the main category schema; and generating queries with respect to the plurality of category schemas based on the main category schema and the subcategory schema.