Data Retrieval With Two-Stage Table and Field Matching
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Solution Overview
Problem
Current data retrieval technologies primarily focus on data table retrieval without performing corresponding field retrieval, leading to incomplete query results that fail to meet user needs.
Innovation Solution
A two-stage query approach is implemented, first retrieving a data table set based on user queries, data table summaries, and field names, followed by retrieving a field set from each data table, ensuring both data table and field retrieval results are returned.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If only data table retrieval is performed, then the retrieval process is simple and fast, but the query results are incomplete and fail to meet user needs
Solution Approach 1:
The retrieval process is segmented into two distinct stages: data table retrieval stage and field retrieval stage. The first stage retrieves candidate data tables based on user queries, while the second stage retrieves relevant fields from those tables. This segmentation allows each stage to focus on specific tasks, improving overall completeness without creating a single complex monolithic process.
Solution Approach 2:
The system performs preliminary data table retrieval before field retrieval. By first identifying relevant data tables and then extracting specific fields from those tables, the system prepares the retrieval structure in advance, ensuring that field retrieval operates on a filtered and relevant subset of data rather than the entire database, thus improving completeness efficiently.
2Measurement precision
If a two-stage query approach is implemented, then query result accuracy is improved, but retrieval time increases
Solution Approach 1:
In the first stage, the system retrieves a partial set of candidate data tables that are most relevant to the user query, rather than scanning the entire database. This partial action filters out irrelevant tables early, reducing the amount of data that needs to be processed in the second stage, thus balancing accuracy with time efficiency.
Solution Approach 2:
The preliminary data table retrieval stage performs filtering and ranking of tables before field extraction begins. By pre-organizing and pre-filtering the data table candidates based on query relevance, the system reduces the computational burden during the subsequent field retrieval stage, thereby minimizing total retrieval time while maintaining high accuracy.
3Adaptability or versatility
If field retrieval is added to data table retrieval, then user needs are better met, but system complexity increases
Solution Approach 1:
The retrieval system is designed with multi-functionality, where the same retrieval framework handles both data table retrieval and field retrieval operations. The system uses unified query processing mechanisms, similarity calculation methods, and result ranking algorithms that work consistently across both stages, thereby achieving enhanced query capability without proportionally increasing system complexity.
Solution Approach 2:
The system segments the retrieval functionality into distinct modules: a data table retrieval module and a field retrieval module. Each module operates independently with its own processing logic, making the system more adaptable to different query types while keeping the overall structure manageable. The segmentation allows each module to be optimized for its specific task without affecting the other.
Data Source
AI summary
Embodiments of the present disclosure relate to a method, an apparatus, an electronic device, and a computer program product for table retrieval. The method includes determining a table query similarity between a user query and each data table in a database based on the user query, a data table summary, and a field name. The method further includes retrieving a target data table set based on the table query similarity. The method further includes determining a field query similarity between the user query and each field based on the user query and the field name. The method further includes retrieving a target field set based on the field query similarity. Furthermore, the method further includes determining a retrieval result of data retrieval based on the target data table set and a corresponding target field set.