Semantic Nodes in Calculation Scenarios for Non-Summable Key Figures
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Solution Overview
Problem
Higher-level programming languages, such as MDX, face challenges in executing queries efficiently due to abstraction penalties, leading to slower execution speeds, higher memory consumption, and summation errors, particularly when dealing with non-summable key figures and complex operations like Exception Aggregation or currency conversion.
Innovation Solution
The introduction of semantic nodes within calculation scenarios that modify higher-level programming language queries to preserve semantical correctness, allowing for proper handling of non-processable database elements and overriding aggregation functions, thereby ensuring accurate results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If higher-level programming languages are used to provide abstraction and ease of operation, then ease of operation is improved, but execution speed and memory consumption worsen
Solution Approach 1:
The patent introduces semantic nodes as intermediary elements between higher-level programming language queries and database operations. These semantic nodes act as a mediator that translates abstract query expressions into optimized execution plans, preserving the ease of use of high-level languages while improving execution efficiency by enabling smarter query optimization and resource management.
2Ease of operation
If higher-level programming languages are used to simplify operations, then ease of operation is improved, but memory consumption increases
Solution Approach 1:
The patent extracts and isolates semantic information into separate semantic nodes that can be independently processed and optimized. By separating the semantic interpretation layer from the execution layer, the system reduces redundant data storage and memory overhead associated with traditional higher-level language implementations, while maintaining user-friendly query syntax.
3Device complexity
If traditional query processing is used to maintain simplicity, then device complexity is reduced, but measurement precision and calculation accuracy worsen
Solution Approach 1:
Semantic nodes serve as intermediary structures that bridge simple query processing and precise calculation. They capture semantic meaning of queries and pass this information to the execution engine, enabling accurate handling of complex operations like Exception Aggregation and currency conversion without requiring users to write complex code or the system to become overly complicated.
Solution Approach 2:
The patent changes the parameters of query processing by introducing semantic annotations that provide additional context information. These semantic parameters enable the system to adjust execution behavior dynamically, improving calculation accuracy for operations involving non-summable key figures, currency conversions, and exception aggregations while maintaining system simplicity.
4Manufacturing precision
If aggregation functions are applied to all calculated attributes for consistency, then manufacturing precision is improved, but calculation accuracy worsens for non-summable key figures
Solution Approach 1:
The patent applies local quality by making aggregation behavior context-dependent rather than uniform. Semantic nodes annotate specific calculated attributes with their aggregation characteristics, allowing the system to apply aggregation functions only where appropriate (e.g., to summable key figures) while preserving individual values for non-summable attributes like currency conversions or exception flags, thus achieving both consistency and accuracy.
Data Source
AI summary
A calculation engine is described that executes calculation scenarios comprising a plurality of calculation nodes that each specify operations to be performed to execute the query. One of the nodes can be a semantic node that is used to modify a higher-level programming language query that stacks the calculation scenario on top of another semantic node based calculation scenario for operations that call for processing of non-processable key figures. Non-processable key figures being key figures that produce incorrect semantics when processed by higher-level calculation scenarios. Related apparatus, systems, methods, and articles are also described.