Query Predicate Modification Using Column Similarity Constraints
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing database query processing is inefficient due to unnecessary processing of predicate columns with inconsistent timestamp values, leading to increased response time, especially in large tables.
Innovation Solution
An automated process identifies and modifies database queries by determining the association between predicate columns, adding constraints based on their similarity to reduce search space and processing time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If database queries process all predicate columns without modification, then query completeness is maintained, but processing time and response time increase significantly
Solution Approach 1:
The patent extracts and removes redundant predicate columns from the query based on column association analysis. When two columns are determined to be associated (e.g., begin_time and endTime both representing time fields), the system removes one of them from the predicate, reducing the search space while maintaining query results. This directly reduces processing time without sacrificing completeness.
Solution Approach 2:
The patent changes the parameters of the query by modifying which columns are included in the predicate based on their semantic associations. The system analyzes column names and data types to determine associations, then adjusts the query parameters (which columns to search) accordingly. This parameter modification optimizes the balance between query completeness and processing efficiency.
2Measurement precision
If database queries include all predicate columns, then search completeness is maintained, but the search space increases unnecessarily
Solution Approach 1:
The system extracts redundant columns from the predicate based on column association detection. When columns are identified as associated (e.g., east_long and west_id both representing spatial fields), the system removes one column from the search predicate, reducing the search space while maintaining the same result accuracy through the remaining column.
Solution Approach 2:
The patent performs preliminary analysis of column associations before executing the query. By pre-determining which columns are associated and removing redundant ones from the predicate, the system prepares an optimized search space before the actual query execution, ensuring both completeness and efficiency.
3Productivity
If database queries process independent predicate columns, then query simplicity is maintained, but processing efficiency decreases
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously analyzes column associations from the database schema and uses this information to automatically modify queries. The feedback loop detects column relationships, determines redundancy, and adjusts the predicate accordingly, creating a self-optimizing query processing system that improves efficiency while managing complexity through automation.
Data Source
AI summary
According to one embodiment of the present invention, a system for processing a query comprises one or more memories and at least one processor coupled to the one or more memories. The system identifies database table columns in a predicate of the query with a greatest association. The predicate of the query is modified based on an association between the identified database table columns. The modified query is performed to obtain results for the query. Embodiments of the present invention further include a method and computer program product for processing a query in substantially the same manner described above.