Abstract Rule Processing for Entity-Based Query Data
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Solution Overview
Problem
Current database systems face difficulties in processing abstract rules with query results that have an entity-based format, where rows contain multiple data values per column, as these formats are not suited for use as inputs to abstract rules due to varying number of values in each row, which mismatch the fixed logical fields in abstract rules.
Innovation Solution
The solution involves generating value fields to store multiple data values from query results and transforming abstract rules to create executable rules that reference these value fields, allowing each row of query results to be processed as rule inputs, thereby making query results with entity-based formats usable for abstract rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If query results are formatted with entity-based structure (multiple values per column), then data analysis ease is improved, but compatibility with abstract rules deteriorates
Solution Approach 1:
The patent segments the multiple values in each column into separate rows, transforming entity-based format with multiple values per column into relation-based format with one value per column. This segmentation allows the data to be processed by abstract rules while preserving the ability to reconstruct entity-based views for analysis.
Solution Approach 2:
Instead of trying to make abstract rules adapt to entity-based format, the patent inverts the approach by transforming entity-based query results into relation-based format that abstract rules can process. The transformation reverses the data structure to achieve compatibility.
2Ease of operation
If query results use entity-based format with varying values per row, then data interpretation is simplified, but rule processing complexity increases
Solution Approach 1:
The patent segments entity-based data into relation-based tuples, where each value becomes a separate row. This segmentation standardizes the data structure to match abstract rule expectations, reducing rule processing complexity while maintaining interpretability through the transformation layer.
Solution Approach 2:
The patent introduces a transformation mechanism as an intermediary between entity-based query results and abstract rule processing. This intermediary converts data formats bidirectionally, allowing simple interpretation of entity-based data while enabling complex rule processing on transformed relation-based data.
3Ease of manufacture
If abstract rules use fixed logical fields, then rule definition simplicity is improved, but ability to handle variable data structures deteriorates
Solution Approach 1:
Instead of modifying abstract rules to handle variable data structures, the patent inverts the approach by transforming variable entity-based data into fixed relation-based structures. This allows fixed logical fields in abstract rules to process transformed data while maintaining rule definition simplicity.
Solution Approach 2:
The patent changes the parameter structure of data from variable entity-based format to fixed relation-based format with consistent columns and tuples. This parameter transformation enables abstract rules with fixed logical fields to process the transformed data effectively.
Data Source
AI summary
Embodiments of the invention provide techniques for processing abstract rules with query results having rows with multiple data values per column. In general, value fields are created to store the multiple data values of each attribute contained in each row of the query results. The abstract rule is transformed, resulting in a single executable rule corresponding to each row of the query results. Each executable rule references the value fields created to store the multiple data values. Each executable rule is then processed, utilizing the value fields of the corresponding row as rule inputs.