Key Figure Data Filters in OLAP Hierarchies
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Solution Overview
Problem
Current OLAP systems, such as SAP BW, lack key figure data filters, which are essential for complete analytical capabilities, as existing filters cannot effectively handle key figure values during aggregation processes and database operations.
Innovation Solution
Implementing a method that generates key figure data filters by executing prequeries or post-filtering intermediate data, allowing conditions to be transformed into characteristic data filters, and enabling the creation of 'Condition Total' and 'Condition Others Total' result rows, which are not affected by traditional visual filters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional visual filters are used in OLAP systems, then the implementation is simple and executes after aggregation, but the filter cannot affect totals and only omits tuples in the display
Solution Approach 1:
The patent combines the functionality of visual filters and data filters by allowing conditions to operate in both modes. The condition framework merges the simple implementation of visual filters with the powerful data-affecting capability of data filters, enabling a single mechanism to both filter displayed tuples and affect aggregation totals.
Solution Approach 2:
The condition feature is designed to be universal, serving multiple functions: it can act as a visual filter when no aggregation is requested, and as a data filter when aggregation is requested. This multi-functionality allows the same filter mechanism to adapt to different analytical needs without requiring separate filter types.
2Ease of operation
If conditions are implemented as visual filters only, then the execution is simple after aggregation, but the filter cannot affect aggregation totals
Solution Approach 1:
The system dynamically adjusts the behavior of conditions based on the aggregation context. When aggregation is requested, conditions transition from visual filter mode to data filter mode, automatically affecting the aggregation process. This dynamic adaptation ensures that conditions reliably affect totals when needed while maintaining simple execution when aggregation is not requested.
Solution Approach 2:
The patent changes the operational parameters of conditions based on the aggregation context. The same condition can operate with different parameters: filtering only displayed tuples when no aggregation is requested, and filtering both displayed tuples and affecting aggregation totals when aggregation is requested. This parameter change resolves the contradiction between execution simplicity and aggregation accuracy.
3Adaptability or versatility
If key figure data filters are added to OLAP systems, then complete analytical capabilities are achieved, but the system complexity increases
Solution Approach 1:
The condition framework serves as a universal filter mechanism that handles both characteristic-based filtering and key figure-based filtering. Rather than implementing separate filter types for different analytical needs, the system uses a single condition mechanism that adapts to different filter requirements, thereby achieving complete analytical capabilities without proportionally increasing system complexity.
Solution Approach 2:
The patent introduces an intermediary layer in the form of the condition framework that mediates between the simple visual filter implementation and the complex requirements of key figure data filtering. This intermediary translates various filtering requirements into a unified processing model, achieving versatile analytical capabilities while managing system complexity through abstraction.
4Measurement precision
If conditions act as data filters affecting aggregation, then accurate condition totals are achieved, but the implementation becomes more complex than visual filters
Solution Approach 1:
The system dynamically adjusts condition behavior based on aggregation context to achieve accurate condition totals. When aggregation is requested, conditions automatically transition to data filter mode, ensuring accurate inclusion in aggregation calculations. This dynamic behavior provides measurement precision without requiring permanently complex implementation, as the complexity is activated only when needed.
Solution Approach 2:
The patent changes the operational parameters of conditions based on whether aggregation is requested. The same condition infrastructure operates with different parameters: simple visual filtering when no aggregation is requested, and precise data filtering affecting totals when aggregation is requested. This parameter-based approach achieves measurement precision while managing implementation complexity through context-dependent behavior.
Data Source
AI summary
A system and method of key figure data filters are presented. The key figure data filters are implemented in an analytical engine of a business warehouse system. The key figure data filters employ conditions, which can be expressed as a kind of selection that describe a set. A key figure data algorithm can be implemented by the analytical engine using the conditions, yet still respect hierarchies in the business warehouse database.