Merging Stacked Multiproviders in Database Calculation Scenarios
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Solution Overview
Problem
Existing database processing systems face challenges in accurately merging multiproviders in calculation scenarios, leading to incorrect determination of row count attributes when stacked multiproviders are merged, which complicates the optimization of calculation scenarios.
Innovation Solution
The system maps and annotates row count attributes of child multiproviders to parent multiproviders, allowing for the differentiation between count keyfigures, sum keyfigures, and calculated keyfigures, and merges stacked multiproviders by adding part providers to the parent multiprovider and removing the child multiprovider as a calculation node, thereby optimizing the calculation scenario.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If stacked multiproviders are merged in calculation scenarios, then the calculation scenario is optimized and intermediate results are eliminated, but the row count attributes may be incorrectly determined
Solution Approach 1:
The patent segments the row count determination into three distinct types: count keyfigures (for part providers only), sum keyfigures (for other multiproviders only), and calculated keyfigures (for both). This segmentation allows each type to be handled with the appropriate mapping logic during multiprovider merging, preventing incorrect determination while enabling optimization.
Solution Approach 2:
The patent changes the parameter mapping behavior based on the type of keyfigure and the source of data aggregation. When merging multiproviders, the system dynamically adjusts which keyfigure parameters are mapped to the parent multiprovider based on whether the child aggregates from part providers, other multiproviders, or both, ensuring accurate row count determination throughout the optimization process.
2Adaptability or versatility
If child multiprovider aggregates from both part providers and other multiproviders, then comprehensive data aggregation is achieved, but complex mapping logic is required for keyfigures
Solution Approach 1:
The patent applies different mapping rules to different keyfigure types based on the local characteristics of data aggregation sources. Count keyfigures are mapped only when aggregating from part providers, sum keyfigures only when aggregating from other multiproviders, and both are mapped when aggregating from both sources. This localized quality approach simplifies the overall complexity by providing clear, context-specific rules.
3Ease of manufacture
If traditional merging approach is used without differentiation, then merging process is simple, but accuracy of data record quantity determination deteriorates
Solution Approach 1:
The patent performs preliminary classification of keyfigures into count, sum, and calculated types before the merging process. This preliminary action establishes the correct mapping strategy in advance, allowing the actual merging to proceed systematically while ensuring accurate data record quantity determination without requiring complex real-time decision logic.
Data Source
AI summary
A method may include executing a calculation scenario at a database. The calculation scenario may include a parent multiprovider aggregating data from a child multiprovider. The calculation scenario may be executed by mapping a first count keyfigure of the child multiprovider to a first sum keyfigure of the parent multiprovider if the child multiprovider aggregates data from part providers but not from multiproviders. A second sum keyfigure of the child multiprovider may be mapped to the first sum keyfigure of the parent multiprovider if the child multiprovider aggregates data from multiproviders but not from any part providers. The first count keyfigure of the child multiprovider may be mapped to a second count keyfigure of the parent multiprovider and the second sum keyfigure of the child multiprovider may be mapped to the first sum keyfigure of the parent multiprovider if the child multiprovider aggregates data from part providers and multiproviders.